94 citations · 231 across the 6 of their papers we have counts for
3 papers · 1 filter
Scaling Up Models and Data with and
Adam Roberts, Hyung Won Chung, Anselm Levskaya +40
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can…
High-Fidelity Image Generation With Fewer Labels
Mario Lucic, Michael Tschannen, Marvin Ritter +3
Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimen…
Self-Supervised GANs via Auxiliary Rotation Loss
Ting Chen, Xiaohua Zhai, Marvin Ritter +2
Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labeled data. In this work we exploit two popular unsupervis…