Mutually Exclusive Learning for Generators with Multi-Label Classifiers
Digya Acharya, Hera Siddiqui, Eduardo L. Pasiliao, Chaity Banerjee · 2023
In this work, we introduce the idea of “mutually exclusive learning” and formulate it as a multi-label classification problem. We provide two implementations of the mutually exclusive learner; the first using a consensus based multilabel framework and the second using an adaptive multi-label framework. We use the mutually exclusive learning paradigm for designing both generators and discriminators (classifiers). We experiment with the MNIST dataset for both classifying the digits using a mutually exclusive classifier and generating handwritten digits using a generative adversarial network (GAN) trained with a mutually exclusive discriminator implementation. Our results establish that GANs trained with a mutually exclusive discriminator converge faster than the corresponding GAN with a standard discriminator. Furthermore, the quality of the generated images is also visually better than those generated by a regular GAN.