Learning Robust Representations for Computer Vision

Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy, Jayaraman Jayaraman Thiagarajan; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1784-1791

Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional subspaces and distance metrics. 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.