# Learning Robust Representations for Computer Vision

arXiv: https://arxiv.org/abs/1708.00069 · HTML: https://mlsanity.com/paper/1708.00069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Rare-Aware Autoencoding: Reconstructing Spatially Imbalanced Data](https://mlsanity.com/paper/2604.02031/) (2026)

## Abstract (arXiv)

Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in the data can frustrate these approaches by obscuring the latent spaces. Our main goal is deeper understanding and new development of robust approaches for representation learning. We provide a new interpretation for existing robust approaches and present two specific contributions: a new robust PCA approach, which can separate foreground features from dynamic background, and a novel robust spectral clustering method, that can cluster facial images with high accuracy. Both contributions show superior performance to standard methods on real-world test sets.