# Adversarial Fisher Vectors for Unsupervised Representation Learning

arXiv: https://arxiv.org/abs/1910.13101 · HTML: https://mlsanity.com/paper/1910.13101/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Fast Adaptation with Linearized Neural Networks](https://mlsanity.com/paper/2103.01439/) (2021)
- [Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search](https://mlsanity.com/paper/2208.13653/) (2022)
- [Learning Representation from Neural Fisher Kernel with Low-rank Approximation](https://mlsanity.com/paper/2202.01944/) (2022)
- [Discriminative-Generative Representation Learning for One-Class Anomaly Detection](https://mlsanity.com/paper/2107.12753/) (2021)
- [Aggregation Schemes for Single-Vector WSI Representation Learning in Digital Pathology](https://mlsanity.com/paper/2501.17822/) (2025)

## Abstract (arXiv)

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. To be concrete, in the EBM formulation, the discriminator learns an unnormalized density function (i.e., the negative energy term) that characterizes the data manifold. We propose to evaluate both the generator and the discriminator by deriving corresponding Fisher Score and Fisher Information from the EBM. We show that by assuming that the generated examples form an estimate of the learned density, both the Fisher Information and the normalized Fisher Vectors are easy to compute. We also show that we are able to derive a distance metric between examples and between sets of examples. We conduct experiments showing that the GAN-induced Fisher Vectors demonstrate competitive performance as unsupervised feature extractors for classification and perceptual similarity tasks. Code is available at urlhttps://github.com/apple/ml-afv.