Transfer Learning in GANs

Key Takeaways

this video is going to introduce the idea of applying transfer learning to generative adversarial networks the transfer learning is one of the most popular ideas in classification tasks such as classifying like a dog or a cat and it hasn't really been applied to generative adversarial networks if this is developed and successful it'll be able to save people tons of computation because you could take a pre train model from something like open AI or deepmind and just plug and play this pre train model into your data set in addition to saving tons of computation this also helped you to train Ganz on small data sets which is really useful in application domains such as medical image analysis so from a high-level overview this is the idea you have the source domain which is the original data set such as image net the Train on and then you transfer this model into some new target so in the context of ganz this means it starts out generating image net images and then it'll transfer in start generating it'll start generating the image net again but then the discriminator will have the new custom data set and it'll start to mold it into the to generating this new data set so this is just a picture of showing how transfer learning is used in classification tasks this is a really popular paper where they repurposed classification layers for the task of object localization so here are some of the interesting things that the researchers and transfer and Gantt study they want to know the impact of the target domain size you know like if you only have a thousand images in your target domain will this still work then they want to know the distance between the source and the target so let's say you go from image net which is pictures of like cats dogs and airplanes stuff like that and let's say you want to translate transition into like liver lesion images this is like totally different so you'd want to see if the distance between these two data sets is going to impact the quality of transfer learning with generative networks and then again you want to look at conditional Ganz so conditional Ganz of this idea that you can inject a class label to control degan output but it's similar to transfer learning with regular classification tasks the label distribution is going to be totally different so you want to see if there's some way that you can labels when you transfer so this quickly just shows how the DC can that does Elsa bedrooms which is 64 by 64 requires 36 million parameters 36 million parameters in a deep model means you need a lot of data to avoid overfitting so evaluating ganz is one of the most difficult things with gain research so the researchers in this paper shoes use the fresh a inception distance which is given here and then independent Wasserstein critic so their experiments they use the improved Wasserstein Gann model which has ResNet layers in both the generator and the discriminator they target 64 by 64 images and they train with a batch of 128 images for 50k iterations so this table right here shows you know the test if training the generator only from scratch or just pre training the generator and then they do the inverse stage test you know pre training only on the discriminator versus training from scratch and then surprisingly they find that pre training works if you're going to only do one of the other it works better than this committer but in all cases it actually does work best to transfer with both the Jenner & discriminator so this picture here kind of gives you an idea of how transferring and Gans looks you see from scratch compared to the pre trained how it's kind of like a more coherent transition now this image shows the transfer process from different source data sets so they're all transferring into kitchens but the one on the top is from scratch I think the one below is from image net and you know this is the different data sets that are describing the paper and this one is a more complete thing that shows the different data sets they test is the source data sets and then the results when they transfer this shows more of that so in conclusion transfer learning and ganz is a really interesting idea it can save computation and data they find that transferring does work well and additionally they find that datasets with dense classes so like maybe a data set that has a bunch of dog breeds would perform better than a dataset in the source dataset that has like a ton of different things so thanks for watching this video please subscribe for more deep learning videos [Music] you