OpenAI CLIP: ConnectingText and Images (Paper Explained)

so here you see a classifier that takes a look at this image and assigns one of many many labels actually one of a hundred and one labels as you can see here and one of the labels is a photo of guacamole a type of food and it assigns a really high probability to that as opposed to like the the second prediction which is ceviche um so you know classifier pretty good okay uh take a look at this classifier out of 397 labels it correctly identifies that this is a television studio um you can go on right here and so this is a photo of an airplane whenever there's a green bar at the top it means that the respective classifier has this correctly whenever there is an orange bar it's an incorrect label with the the green bar being the correct label so you can see here these classifiers perform sometimes pretty well on these examples and sometimes not but what you can distinctly see is that these are all from different data sets so different tasks there is a satellite image there is a car and you're supposed to classify which car it is not only that it is a car so very diverse set of of tasks and the interesting thing is that this is all the same classifier so the this classifier is it's not even fine tuned it is a zero shot classifier that handles all of these different training data sets sorry not training data sets all of these different test data sets in one go so that's already pretty cool but what you may have noticed is that the labels aren't labels that you would usually see in a classifier so you know these these 101 labels here they are it says it here guacamole that's the label interestingly the label the classifier assigns is not just the word it's the a photo of guacamole a type of food okay that's the label the classifier assigns and the second highest label is a photo of ceviche a type of food it's not always a photo though it is often a photo but here you can see for example the label that the classifier assigns is a centered satellite photo of permanent crop land where the um the correct label here is the annual cropland which is down here again the label is longer so there's something interesting going on here it's the same classifier it's zero shot so that means the classifier is not trained on these data sets it's not trained to fulfill these tasks yet still it seems to perform okay and the labels are quite weird so this is this is a new paper by open ai which we're going to look at today you can see it's a pretty long paper but we'll cut it short i promise and it's called learning transferable visual modes from natural language supervision and the model colloquially or also in this paper is referred to as clip so this is the model has been released along with the dahle model which you know can do the chair made of avocado and so on the dolly model is a generative model that generates images clip is a more of a i don't want to say discriminative model but clip is a model that takes in images and text and connects them in a in a non-generative way so we're going to see what that entails it's by alec radford and john woo kim and others as i said of open ai so the idea here is to connect text and images and this has been done in a in a number of ways previously even in this way it has been done in one fashion or another i find the introduction and discussion of lady related works in this paper to be very very thorough and and superb so they do assign a lot of credit to people who have had the various ideas so the goal here is that we want to get a model that can represent images and text really really well okay so how do we connect images and text first of all what what if what if we have a data set of images and text okay so they construct a new data set where there's an image like something like this a cat and a text a little piece of text to it like my my cute cat images and text like this you'll find on you know for example social media you can scrape that pinterest what not flickr people write descriptions along with their pictures so it's pretty easy to get these pairs of images and text from the internet without having to label them right so one motivation of doing this kind of work is if we train a image classifier model we always need labeled examples into you know into a very predefined set of classes so in imagenet we have a thousand classes or twenty two thousand respectively in mnist we have ten however if we could just somehow learn uh to connect images with the text that comes along um we wouldn't be bound by the classifier labels and we could get very good representations so the original idea or one of the original idea is we take the image and we predict predict the text from the image um of course dolly goes the other way so um dolly some somehow goes the other way taking the text and predicting the image but the idea is if we can take an image and from it predict the text what we get out of it is not only a model that can label images but what we hope to get out of it is this process right here may be very very good representer so if this is like the image goes into a neural network with a bunch of layers and then out comes you know the text my cat and so on then somewhere in here in the intermediate representation of the neural network there must be a pretty pretty good representation of what is in the image so not not only you know the pixel values um but there must be actually some kind of representation of the concept of cat because otherwise it could not predict uh the word cat at the end okay so the idea is to get a really good representer and then you could take that representation and fine-tune it to other tasks and so on so that's one of the ideas we're going to work off here and it turns out this is pretty useful there have been papers before predicting the um simply predicting the caption of images but it doesn't work too well so what this model here is going for and we're we'll simply um we'll simply let's look at this graph right here so they tried first to predict the text and you can see that zero shot and we're going to to look at what exactly zero shot image net accuracy means in this context but you can see here that they had some success with using a a transformer language model to predict the text in images and evaluating that on on imagenet however they seem to have more success by using just a bag of words predictions so what that means is you you're not trying to predict the exact words you're simply trying to predict which words occur in the description so you see the photo if you predict cat and my and cute in you know any not non-ordered you're already correct and that already gives a sort of a better efficiency you can see the models here they tend to go up uh but it's questionable if that will ever reach the orange line and with their new objective with what this paper suggests you can see right here the contrastive method you get a way bigger performance so we'll look at what this zero shot accuracy means and why it might be that these simply predicting the text from an image might not be a good enough idea so let's say we have a model that can do this we have a model that can take an image and it can predict uh the the text that appears in it right most of the time this model right here is also going to give you something like a probability okay like a likelihood so if this is a a transformer you can you can ask you know for its logits and then you can compute the likelihood of a given label so if you have such a model what you can do is exactly what what they allude to right here if you have an image task right and you have a you have a model that can predict the the text of an image you can take that image and you can run this sort of through your image and through your encoding pipeline and then you can ask the model instead of you know predicting a text you can ask the model how likely is the text um dog how likely is the text cat for this image how likely is the text mouse and then you can you get some sort of likelihood right so maybe it says dog is this likely cat is this likely mouse is this likely and immediately you have built a classifier so i hope you can see if if i have a model that can predict how likely a piece of text goes with an image i can by simply asking my model for each of the for each of the classes that are possible in the task i immediately get a classifier out of that i mean i'd have to normalize or something by that but i immediately get a classifier um and now you can also already see why we might want to phrase the things a bit so i don't want to just put dog and cat right here even though those are the labels in that task right if if i had an imagenet classifier i would put here i would put all of the 1000 possible classes and ask the model for each how likely is that label to go with this image and the model you know can produce text but the model can not only produce you know the single word dog the model can also tell me how likely is the phrase a photo of a dog a photo of a dog or how likely is the phrase a photo of a cat and so on right so um and you can you can see that this result here the classifier result it might change actually depending on how you phrase so here you can use the exact same classes as you used above but by rephrasing the prompt so to say you might get a better quality classifier or a worse quality classifier so if you already know that your images are all photographs um you will get a better accuracy because simply you know the the model if you you might get a better accuracy by asking the model hey how likely is the phrase a photo of a dog going with this image versus the phrase a photo of a cat that might give you a better signal so less noise in whatever you get as an output than simply going with the single word because again this model is trained to predict this just from a data set scrape from the internet so how often do people you know post something i don't know an instagram of their cat and simply write cat with it whereas you know maybe they they were right here's a photo of my cat right so the the phrase a photo of a cat is or they do like hashtag photo hashtag cat or something like this so that's why these classifiers at the bottom they were constructed from the labels of the data set but with a prompt that has been adapted by humans uh to work you know find to work particularly well on that data set so we're sort of back to prompt engineering here so this is how we go from a model that can assess predict text to a classifier and that's a zero shot classifier we don't need to train this classifier on the actual task we simply need to restrict its possible outputs to the classes at hand right um this is a bit it's a bit like a tiny bit like like you know in in queue learning in uh where for in in each step you ask your model well what if i do action one and then the model tells you what that's five good probably that your q value is five and then you just ask well what if i do action two and then your the model says well that's seven good and so on so it's it's sort of a similar concept uh in except you know q learning we usually train end to end with an actual classifier but i said simply predicting text objective might not be good enough right so we're going to retain this property of being able to zero shot classifier uh but we're going to now switch out our task of how we get to such a model so instead of predicting text what does clip do clip does the following so what we're going to do is we're going to take the image right here and we're going to pass it through an image encoder and that gives us an image representation so a vector in some latent space so this is image one and then image two right here would be image two here okay so we have a mini batch of images and that's important um then we're going to take the text and feed it to the text encoder also obtaining a representation for the text right a single vector for this entire text right here and then of course if we go to the second sample in the mini batch we get the second representation and the batches of course in the training data set we know that the first the first text goes with the first image the second text goes with the second image the third text goes with the third image because that's how we scraped it from the internet and then what we ask the model to do is simply to tell us not so previously we tried to predict from the image the text right we went through the image encoder and from this representation here we try to predict the text so we no longer do that what we're trying to do is simply ask ask the model which for so for this representation which of these texts is most appropriate to that particular image okay so this is why it's called a contrastive objective we know because this is training data we of course know that image one goes with description one and image two goes with description two but we're going to train this in the way that you know we feed in this image and we ask it to which of all of these texts right here to which of all of these is this image the closest and we're going to train it such that it is maximally close to the correct one and minimally and far away from all the other so this this is why it's contrastive it contrasts what we know goes together right the diagonal elements in this matrix with what we know doesn't go together in actually we don't know if a different description wouldn't fit the same image but we can safely assume that a random piece of text since we do the mini batches randomly a random piece of text will probably not go with this particular image at least not as well as the piece of text that we found it with on the internet okay so you get what you get is effectively for each input you get a classification task in this direction you can see right here for image three there is one correct text that it goes with and for each text you get a classification task in this direction by the way this is simply an inner product right here right you're simply trying to maximize the inner product of things that go together and minimize the inner product of things that don't go together so you you multiply the two for the inner product you interpret that as a logit and then you do a soft max classification in this direction and the softmax classification in this direction so this is a symmetric loss from the text and image perspective and yeah so so it's a classification problem like a classification problem viewed from two different angles so you can immediately see that this relies on having large enough mini batches right so the larger your mini batch as your mini batch size approximates the entire data set your representations are going to be more and more detailed right so so you wanna so pepper the aussie pop being close together to this particular image means that in the ideal case it is close to this image and far away from anything else in in the data set and as an approximation far away from anything in this particular mini batch and at inference time you do very much what we did so far so you take if you want to build an image classifier and the interesting thing is you can also build a text classifier right if you have multiple images to go with a text then you uh you can do that it's entirely symmetric but in this case you take an image you put it through the image encoder you get a representation here you get all the labels of your classification tasks right so this is the label is this right here you engineer a prompt and that you do as a human right this is heuristic this you as a human think aha okay i'm going to put whatever this is here you encode all of these labels in their prompt context through the text encoder you get the representations here and you simply ask to which of these labels is it closest right so the is the inner product the highest and then and that's how you obtain the label zero training needed on the actual task right so the data set that you do this with can be an entirely different data set that then you do this with um and this is extremely extremely uh interesting i've actually seen um some some posts on on twitter and reddit where people use this to guide a a style gan to produce given pictures with given descriptions and so on so the possibilities for this are uh pretty pretty huge okay so that's uh that's the model the model it encodes images and codes text it does this contrastive objective what goes together what needs a part and now you see why this might be a better representer than for example simply pre-training a model on an image classification task because if you pre-train a model on an image classification task it is going to simply lump together every all the dogs you know if this is if this is your classification test it's going to lump together all the dogs because there's no need to differentiate the individual dogs from each other right it's going to lump all of them together and forget that they are actually different right it's also going to forget everything that doesn't concern the immediate classification problem whereas this model here this model is specific as as it gets better and better it will pick up at more of the text right so in in this case maybe if the model is pretty weak still it will focus on this pup and that's about the same as saying okay it's a classifier of a dog but then we can also aussie pop if it incorporates that if it gets better well it can differentiate it from other dogs and by the way it's a pop so it's a young dog um it can also learn eventually learn its actual name right and and so on so you can see that as the model gets stronger it can pick up more and more nuances of the data set so they test this and they test it fairly fairly fairly extensively um and i don't think we'll have to go through all of it for me to convince you that this is a good idea you're going to maybe see it approximately or immediately so um yes so they use different different types of yes that's what i wanted to say they use different types of encoders for the image encoder so for the text encoder this is a transformer so transformer it's not a particularly big transformer even and they simply take the end of sentence token the representation of that at the end and that's their vector if you don't know what a transformer is i've done many many videos on transformers um find one of them any of them for the image encoder they test out a bunch of different things so they test out a bunch of variants of resnet i've done a video on that and they also test out a bunch of variants of the visual transformer the the vit that has recently been popularized i've also made a video on that so that's why their model shows up in sort of different uh flavors and sort of different different points here they scale the amount of data i believe with the model so they scale everything together compute data and model size and that's why you see different variants of the same model they also do ensembling so you know you have to engineer these prompts and um what you can do is you can engineer better prompts and that will gain performance and you can also ensemble over prompts and you can see right here that that uh gets you both an efficiency gain if you want to stay at the same performance and also um sorry yeah and also it gives you a performance improvement for the same compute um with the same model right so here the corresponding dots are the same model that's why they have the same compute so that's just one of the fun things you can do and again i think prompt engineering will become quite a bit more relevant so here you can see you can see the comparison zero shot clip um is competitive with a fully supervised baseline right so the baseline here isn't too good so it's a fully supervised linear classifier fitted on resnet 50 features on 16 data sets including imagenet so the resnet 50 is a popular architecture it's not nowhere near the absolute best we have but it's a popular uh architecture so this resonant 50 what it's what it has been trained on is has been trained on imagenet right so you get so and that results in a neural network with a bunch of layers including a classification layer at the end right into a thousand classes so what you do is you pre-train this on imagenet and then you simply take this part right here up until the last layer and you take it so that's this part right here and you assume that this has a sort of a good representational power since it can do imagenet and then you simply train a new linear classifier on top that does the classification into whatever new task you want so this is called um it's called linear probing so linear probing you can also do it in the middle uh sort of but in this case they mean linear probing at the second to last layer like before the classification layer so you assume that whatever this is is a good representation function you keep it constant and then you train a linear probe on top of it this is compared to fine-tuning where you would fine-tune the entire network on your new task but they elect to do most of their experiments with linear probing since it gives you a better indication of the representational power of the bases so here they compare to image net right so on six and that is including image net so for imagenet you would expect the resnet50 to perform quite well because it's been its representational base has been trained on imagenet and training a linear classifier on top it should simply give you back the performance that it had on imagenet here you can see how zero shot clip compares to linear probe on resin at 50 right zero shot clip compared to an actual trained thing not not the best but a trained thing and you can see that on many many many data sets clip out performs the resnet 50. zero shot right um so no training required beyond the pre-training that being said the pre-training is huge um but it's similar to gpt-3 right you train it once huge training but then you can do lots of things imagenet interestingly you see right here only it's actually improving imagenet over resin at 50. crazy right um whereas so resnet50 still better in various uh other tasks so this is not to say that this is the new state of the art or anything except in stl 10 where it actually appears to be the new state of the art against all the previously including all the supervised whatever it's the new state of the art on this data set and the reason is this stl 10 data set it has very few training examples per class only so supervised is very difficult transfer learning is kind of difficult as i understand it it's not that similar to imagenet so that transfer learning is kind of different so this really seems to be this zero shot clip objective seems to be good if you have um images that are sort of natural uh that happen a lot on the internet but are not really like imagenet um so there exists quite a number of those and that you have few labeled examples of if any right so that's a that's a good application domain however on more specialized things they say things like you know tumor classification and so on satellite images this clip objective still does pretty poorly uh probably because you know that that's not the type of images you find on the internet with a piece of text super interesting mnist one of the easiest tasks in deep learning it also quite underperforms in this in this thing so that they do they do an analysis of these different data sets so they they compare to resnet50 and also to visual n grams right here and they discuss the the importance of the different data sets oh i find i found this too i found this to be very interesting uh most standard image classification that data sets treat the information naming or describing classes which enables natural language based zero shot transfer as an afterthought uh the vast majority of data sets annotate images with just a numeric id of the label and contain a file mapping these ids back to their names in english some data sets such as flowers and the gtsrb as it's a german transport street sign or data set i don't exactly know don't appear to include this mapping at all in their released versions preventing zero shot transfer entirely so what these authors had to do is they had to like look at the classes and then sort of label them themselves because their model works on language whereas this street sign data set probably just came with this is sign type one this is sign type two they have a footnote here alec learned much more about flower species and german traffic signs over the course of this project than he originally anticipated i love that i love a bit of humor in the papers and i so i made this meme um where the street sign is specifically tractors and trucks with an authorized loaded weight of more than 3.5 tons prohibited i wonder actually how the model does on exactly this uh sign but yeah we'll find out by the way the clip model is available in not the big one but a small one is available actually trained so you can test it out and maybe we'll do a video on it where we actually do something with it so here you can see that if they compare their model to few shot linear probes so here they compare zero shot clip with few shot linear probes so before we compare to linear probe which mean means we just trained this linear classifier but we did it on the whole data set okay so here we simulate only having very few examples per class which is where pre-training really comes in and you can see that zero shot clip outperforms a lot of models if you only give them very few labeled examples per class in fact it is comparative to a 16 it is comparative to a 16 label bit m so this is one of the best models that is currently in the public and that is doing this transfer learning so if you transfer learn with a linear probe again this is not fine tuning with a linear probe on 16 samples per class with this model you are still only as good as the zero shot no training at all of the clip model that is pretty pretty interesting and pretty cool the other noteworthy thing is that if you linearly probe the clip model you way outperform the um the largest models there and also what is also interesting is that when you do labeled examples for clip when you do linear probe on clip the performance decreases first and only increases once you get to like four labeled examples per class and that you know is um is pretty intuitive when you think about it so what you're doing is so in clip the zero shot classifier is actually a different one than the linear classifier so the zero shock classifier is in a way already trained so it has already trained this sort of last layer where as if you do linear probing you throw that away you know the whole part where you encode the text and you blah blah blah you throw that away and you simply do the old school so the linear probe here this is no more of the is which text is closed this is simply i take this i throw away the last layer i put in a new last layer and i do my original classification task and of course this layer right here is initialized randomly and it's going to require some training and maybe you know one example per class isn't enough it's it's just going to pick up on some spurious uh correlation in the future and it's going that's why it's getting worse initially but it recovers at four examples per class and it severely outperforms the other models so we'll forgive it um they do discover in various experiments here that it is very very different from data set to data set how this model performs zero shot how it performs versus linear probing they they find that um they find that very often in in um in some data sets uh that are far away from sort of natural images they perform worse in again in some data sets they require lots of labels to match zero shot performance so it is really a study into sort of um i want to say it's a study into what kind of images appear on the internet they do interestingly there is a trend in machine learning that if you give more data and compute then your error goes down even with the same type of models and that seems to hold pretty well here as you can see here as they scale up this is the same this is a resnet backbone as you scale that up zero shot clip performance scales smoothly as a function of model compute however they do note that there is a whole bunch of variations so the curve you're seeing is the average but for the individual tasks in their task data sets um it it varies wildly so there's a lot of noise here this could be because of how the data sets are selected this could be because of how the prompts are engineered there is still a lot unknown right here they compare various other things like linear probe linear pro performance of clip models in comparison with state-of-the-art computer vision models and they do outperform all of these other models as you can see here so there is 12 data sets in previous experiments but the 12 are still sort of similar to imagenet but if you include more data sets of course that's sort of a selection bias or whatnot but then this model severely outperforms all of the other models so the red models here are the red ones are the clip models compared to the other ones so yeah this seems to be a step forward in the sort of in the sort of building classifiers for the average user right so i can now go ahead take this model and build my own classifier pretty pretty easily they also make some interesting discoveries in terms of robustness robustness to perturbations so previously all these models they sort of pre-trained on imagenet and so on and people have discovered that as soon as you go away from imagenet these the performance of these models decreases heavily so if for example imagenet v2 is just imagenet but is it they try to collect i've made a video about that by the way they try to collect imagenet as closely as possible to the original test set they try to collect a new test set and immediately the performance of all the classifiers dropped in the light of this just slightly data shifted data set um and if you if you sort of try to go away a little bit further so you just have sketches of these objects um you sort of have this this adversarial placement of objects you can see right here uh it's you know it's pretty it's pretty mean but still a human could do this right um you see right here these are just variations on the themes of imagenet they have the same classes so a classifier trained on imagenet should be able to also classify these images right so here they compare zero shot clip to models that have been trained on imagenet and they find that zero shot clip even though it matches so this zero shot clip matches the performance of imagenet by the way a huge achievement right this is a fully trained model on imagenet and this is a not the state of the art but respectable top one performance on imagenet and zero shot classifier matches that performance this is crazy okay uh you can see as this classifier degrades degrades degrades degrades degrades as you go to harder and harder data sets that are all technically imagenet images like in the same classes this classifier it sometimes even you know gets better but it you know it keeps up its performance which you can see here the difference uh between it gets just larger and larger so the clip is way more robust and of course this model right here is trained to predict these specific types of images so it knows very well like how to keep them apart the only thing it has to do as a classifier of imagenet is keep apart the individual instances of exactly those classes in exactly this data set so it forgets about everything else right and as a result it he has never seen a sketch it it like a banana is yellow what are you talking about um so it heavily degrades right and whereas clip it simply knows how to sort of connect images to text so while clip realizes that of course both are described as banana it somehow has to account for the fact that there are also lemons in here right it has to somehow represent that um it has to represent that this is a bunch of fruit and that this is here maybe a you know high-grade picture like on a magazine where this here might be more of a sort of random gopro fallen into some bunch of bananas it has to somehow represent all of this if it you know performs well on its task and thereby its representation will be nuanced enough such that it can transfer more easily it picks up on different features uh oh than only distinguishing banana from you know other classes in the imagenet data set and that results uh so here is the the curve in that if you had the ideally robust model you'd have this right here so the exact same performance on the natural distortions than on imagenet in the original imagenet you can see that all of the standard imagenet training examples including all the robustness techniques that barely lift away from this curve are massively outperformed by a zero again a zero shot classifier that hasn't even been trained on imagenet and the fact that it hasn't been trained on imagenet might be one of the you know things that it actually is is very helpful so they do they do some investigation into it in including that you can in fact um adapt to imagenet so you can in uh i think that's the that's a linear probe if if you linear probe clip you can improve the performance on imagenet where interestingly you can improve the performance on imagenet by doing a linear probe on top of clip so this is logistic regression clip while only mildly um degrading your performance on these other data sets so there seems to be a value to only to just having their representation so their representation itself seems to be more stable okay so you can see as you adapt to imagenet this performance improves massively but it only degrades a little bit across the other data sets so that means yeah as i said the representation itself is more nuanced such that even if you train a linear classifier on pure classification you'll still keep up the performance on the other tasks you can also adapt to class shift so by better prompt sort of prompt engineering for some of these sub tasks but i think that's a sort of a minor thing all right um yeah i don't want to go you know too much they also compare to humans which is very interesting and they discover that you know samples that are hard for the clip model are also hard for the human model they do some sort of duplicate detection from their training data set because their training data set is 400 million images together with text right so it's conceivable that there's some duplicates but they find even if there is there's generally not a problem and they have like a three or four page broader impact section as you can see right here which you know is um so if you read it it reads sort of like um yeah there are problems with these models we are better than other models but we're still not good enough or things like this or they always they're like yeah this is of course we're better like they're better at everything but then again you know this is only preliminary more study is needed and so on but i so they have some fairly um interesting interesting results so they what they do is since there is such a focus on prompt engineering right um it actually matters what you give to the model as possible labels so this is no longer fixed labels you can give any labels so they have these data sets where you you know for example this fairface fairfax race where you try to categorize faces into different uh ethnic ethnicities or races um these seven things that are given here they also include some non-human categories or is it so they include they include categories such as here such as animal chimpanzee gorilla or angutan and they also include sort of crime categories like thief suspicious person criminal and then they research how how the model mis behaves and these models they do do a fair bit of you know kind of misclassification right here as you can see they also so they notice that the misclassification is especially there for younger people so these are the ages of people and here are the misclassification rates you can see the misclassifications are mostly for younger people then they simply add a child category and then the misclassification for young people all of a sudden drops because the model now has the option to classify them as a child so this i think the result of the paper and especially of the broader impact section one of the results is that it matters a lot how you engineer the prompts which is something we already knew but of course this can be particularly particularly crucial in some applications uh in some concerning applications that's kind of one of their points right here you can see that the paper is huge and it also has a huge appendix and they do as i said a lot more experiments right here um but all in all this is a very very cool approach i feel and it's as i said a step towards making it easier for you know the everyday person to build their own classifier for you know you can do quite niche tasks as long as they're sort of natural images this will work fairly fairly well i think it's pretty cool it um gives it gives a little bit of more freedom in how you work with these models and i'm excited for people to come up with ideas of how to use this how to connect this to other models such as you can connect it as we already saw with dali um you can connect it with stylegan as some people are doing i'm sure you can connect it to something like gpt3 and it's going to be an exciting world all right that was it for me thanks bye

Channel: Yannic Kilcher

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Keywords: deep learning, machine learning, arxiv, explained, neural networks, ai, artificial intelligence, paper, openai, sutskever, radford, meme, dalle, dall-e, images, vision, text, nlp, natural language processing, resnet, vision transformer, transformer, visual transformer, sota, state of the art, zero shot, zero-shot, few shot, few-shot, unsupervised, contrastive, simclr, efficientnet, noisy student, representation, embedding, latent, natural language, prompt engineering, bias, scale, distribution shift

Id: T9XSU0pKX2E

Length: 48min 6sec (2886 seconds)

Published: Tue Jan 12 2021