Must Read Papers on GANs

Key Takeaways

this is a deep learning video on must read papers on ganz from Henry AI labs generative adversarial networks from one of the most interesting ideas in deep learning showing you how you can generate never-seen-before images from completely random vectors the first paper in the list is DC Ganz from Alec Radford at all this paper builds in the original gun idea by showing how you can use up sampling convolutional layers in the generator and regular convolutional layers and discriminator this extends the architectural and like the you know neural network complexity of the gann models individually as well as proposing these additional guidelines for building convolutional jenner of adversarial networks these include things like using batch normalization as well as Braille ooh and leaky random of activation functions following DC GN paper second paper and our list has improved techniques for training ganz from Tim Salomon's at all.this this paper proposes some heuristic ways of improving the dcen training these include feature matching where instead of just trying to directly the generator instead of just directly minimizing the error on the real first fake - from discriminate it tries to match the internal activations and the discriminator of real images the minimap discrimination is the idea of having some kind of additional feature map that corresponds to the batch statistics of the generated and real images historical averaging just make sure that you know one faulty parameter the signal from the like radiance signal from the discriminator doesn't change the parameters of the generator too much and vice-versa one side label smoothing is just to prevent overconfident predictions and discriminator and then virtual bachelor emulation is use a reference bash for the games and bias parameters of bachelor musician and this is done to reduce the internal influence that batches might have on each other during generation so third paper in this list is conditional Ganz conditional Ganz is one of the most interesting ideas and Ganz showing how you can use supervised learning in this framework so you do is you would concatenate the one hot class label such as if it's see fart n you know like the one zero zero zero indicating that it's a truck image and then zero one zero zero zero indicating it's a car image you discriminate this into the intermediate Meishan can control the output of the game it's falling this is the progressive growing of Gans this is a really interesting paper that became really popular because it showed how you could generate high-resolution celebrity faces which is a really unprecedented thing that some people couldn't believe that the paper the faces don't really exist and they're completely generated by these deep learning models so the idea here is that you can take the high-resolution problem and you can decompose it to the sub structures of low resolution because as a ton of correlation between a 1024 by 1024 image and its downsampled low resolution counterpart so they do is they progressively fade in the layers use and then they use other tricks such as a mini bash standard deviation layer a future map equalized learning rate and pixelize feature normalization so next waiver is big game began as one of the state-of-the-art models taking ideas such as spectral normalization class projection in discriminator and self attention and scaling it up and making you know doing things like doubling the filter Maps increasing the batch size and other things to create these really incredible image made image net generated images following this another paper those regard that's when the state of the art style game salgan uses the same progressive growing model but they switch from mapping from the latent code directly and it said they start from a constant learned value so the way they used the latent code is borrowed from style transfer literature and this idea that you can use adaptive instance normalization to control the style of generated images and this has some really amazing results in the latent space interpolation the next paper in the list is psychic and so I began builds on the idea that if you go from French to English and then English to French you should be at the same sentence so they applied the same concept to further structure the adversarial loss and image to image translation tasks such as a zebra a horse following this is pics depicts another really popular paper in image translation they have other interesting ideas like little india idiosyncrasies for doing image damage translations such as using 70 by 70 windows in discriminator and then another idea is stat get stuck in follows the same multiscale philosophy of you know breaking down the high resolution tasks into 64 by 64 up to 256 by 256 in addition to the stage 1 stage 2 process they also apply they add this multivariate Gaussian to smoothen out the discontinuous text embedding space this overall is really influential for stabilizing Gantt rank and then finally in the list is general adversarial networks the original paper from Ian Goodfellow this paper provides a fundamental understanding of the algorithm as well as other interesting ideas such as non saturating loss functions just thanks for watching please subscribe to this YouTube channel and check out Henry AI labs comm for more deep learning papers