ganhacks

GAN Hacks is an open-source educational reference containing practical techniques for training generative adversarial networks more reliably. Based on research presented around NeurIPS 2016, it summarizes methods for addressing instability, vanishing gradients, and poor generator performance. The guide explains input normalization, modified loss functions, and Gaussian latent vector sampling. It recommends architectural practices involving batch normalization, LeakyReLU, and convolutional upsampling. Additional techniques include label smoothing, noisy labels, experience replay, and instance noise. Researchers can also explore optimizer selection, discriminator training strategies, and early failure detection. The repository includes references to relevant academic papers and implementations. Its recommendations are historically influential but are not maintained as current best practices.