AC-GAN Explained

Key Takeaways

this video will explain the AC Gann model AC ganz build on the idea of class conditional ganz class conditional ganz extend the original Gann framework by adding in prior information in the form of a class label if you have liked CFR ten with ten classes this constant would be a one hot vector with a 1 indicating the class like maybe one in the cat index and then zeros and all the other classes what the AC GN does is it builds on the idea of having a class conditional prior input and it tests the discriminator with reproducing that input so when the generator receives a class label to generate a cat a discriminator has to not only predict real or fake but it also should label it as a cat so this just shows the lost function that's formed now that the discriminator is made up of two parts real and fake and then a classifier that predicts the class labels of the real and generated images so what they do is they take the imagenet data set with 1000 classes and they split it into a hundred different AC cans each with ten classes that they're classifying the labels on so then you experiment with this and they try to take apart and have more classes in each AC gain but they find that ten works the best and as they try to increase the number of classes in each model they have mode collapse in the generator so another thing they want to know is they achieve 128 by 128 images with the AC kin but they want to make sure that this isn't just because of like a naive up sampling like you could really just generate 32 by 32 images with again and then just do a nearest neighbor interpolation and and call it 128 128 so what they do is they test the discriminability and high resolution and low resolution samples to guarantee that they are actually adding information in the higher resolution sampler so what they do here is they down sample images and they feed it to a pre trained inception network that is trained on image net and they have it see like what kind of classification accuracy it achieves on different resolutions and they find that the 128 by 128 model as a much higher accuracy relative to the 64 the 32 and then generally they find that with the real data 84% of the imagenet classes are easier to discriminate at 128 by 128 compared to 32 by 32 and this is interesting too because it thinks about like what if you have like a thousand twenty-four by thousand twenty-four but you couldn't really do that right now because of the model size and the computational complexity of that but is likely that if you had really high-resolution data sets that you would have an easier time with classification models so another cool thing that they do is they show how the nearest neighbor analysis looks so they take these samples from the AC game and then they go and use in l1 distance to grab the most similar examples in the imagenet data set and this shows sort of like the timeline the AC gain compared to the unsupervised ECG and in the left that was improved by Salomon's at all so their paper they don't use any class labels and on the right it shows how class labels in addition to this AC game extension can really improve the quality of generated images so inclusion they have all of they're not all there but they have ten generated images from each class hosted at this link and they discuss how this could be extended to any generative framework and especially audio synthesis so thanks for watching this video please subscribe to Henry AI labs you