Archive for the 'GAN' Category

Generative Adversarial Networks or GANs are very powerful tools to generate data. However, training a GAN is not easy. More specifically, GANs suffer of three major issues such as instability of the training procedure, mode collapse and vanishing gradients.


In this episode I not only explain the most challenging issues one would encounter while designing and training Generative Adversarial Networks. But also some methods and architectures to mitigate them. In addition I elucidate the three specific strategies that researchers are considering to improve the accuracy and the reliability of GANs.


The most tedious issues of GANs


Convergence to equilibrium


A typical GAN is formed by at least two networks: a generator G and a discriminator D. The generator's task is to generate samples from random noise. In turn, the discriminator has to learn to distinguish fake samples from real ones. While it is theoretically possible that generators and discriminators converge to a Nash Equilibrium (at which both networks are in their optimal state), reaching such equilibrium is not easy. 


Vanishing gradients


Moreover, a very accurate discriminator would push the loss function towards lower and lower values. This in turn, might cause the gradient to vanish and the entire network to stop learning completely. 


Mode collapse


Another phenomenon that is easy to observe when dealing with GANs is mode collapse. That is the incapability of the model to generate diverse samples. This in turn, leads to generated data that are more and more similar to the previous ones. Hence, the entire generated dataset would be just concentrated around a particular statistical value. 


The solution


Researchers have taken into consideration several approaches to overcome such issues. They have been playing with architectural changes, different loss functions and game theory.


Listen to the full episode to know more about the most effective strategies to build GANs that are reliable and robust.
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In this episode I explain how a research group from the University of Lubeck dominated the curse of dimensionality for the generation of large medical images with GANs.
The problem is not as trivial as it seems. Many researchers have failed in generating large images with GANs before. One interesting application of such approach is in medicine for the generation of CT and X-ray images.
Enjoy the show!



Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images

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