249 citations · 625 across the 53 of their papers we have counts for
3 papers · 1 filter
Adversarial Fisher Vectors for Unsupervised Representation Learning
Shuangfei Zhai, Walter Talbott, Carlos Guestrin +1
We examine Generative Adversarial Networks (GANs) through the lens of deep Energy Based Models (EBMs), with the goal of exploiting the density model that follows from this formulat…
Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking
Alaaeldin El-Nouby, Shuangfei Zhai, Graham W. Taylor +1
Deep neural networks require collecting and annotating large amounts of data to train successfully. In order to alleviate the annotation bottleneck, we propose a novel self-supervi…
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
Chen Huang, Shuangfei Zhai, Walter Talbott +4
In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric. In this work we assess this…