Adversarial Fisher Vectors for Unsupervised Representation Learning

NeurIPS 2019 Shuangfei ZhaiWalter TalbottCarlos GuestrinJoshua M. Susskind

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 formulation. In contrast to a traditional view where the discriminator learns a constant function when reaching convergence, here we show that it can provide useful information for downstream tasks, e.g., feature extraction for classification... (read more)

PDF Abstract NeurIPS 2019 PDF NeurIPS 2019 Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet