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Found 4 papers, 1 papers with code
Multi-Domain Self-Supervised Learning
In fact, training these methods on a combination of several domains often degrades the quality of learned representations compared to the models trained on a single domain.
Understanding Overparameterization in Generative Adversarial Networks
We also empirically study the role of model overparameterization in GANs using several large-scale experiments on CIFAR-10 and Celeb-A datasets.
Understanding Over-parameterization in Generative Adversarial Networks
In this work, we present a comprehensive analysis of the importance of model over-parameterization in GANs both theoretically and empirically.
Winning Lottery Tickets in Deep Generative Models
In this paper, we confirm the existence of winning tickets in deep generative models such as GANs and VAEs.
