Switchable K-Class Hyperplanes for Noise-Robust Representation Learning

Abstract

Optimizing the K-class hyperplanes in the latent space has become the standard paradigm for efficient representation learning. However, it's almost impossible to find an optimal K-class hyperplane to accurately describe the latent space of massive noisy data. For this potential problem, we constructively propose a new method, named Switchable K-class Hyperplanes (SKH), to sufficiently describe the latent space by the mixture of K-class hyperplanes. It can directly replace the conventional single K-class hyperplane optimization as the new paradigm for noise-robust representation learning. When collaborated with the popular ArcFace on million-level data representation learning, we found that the switchable manner in SKH can effectively eliminate the gradient conflict generated by real-world label noise on a single K-class hyperplane. Moreover, combined with the margin-based loss functions (e.g. ArcFace), we propose a simple Posterior Data Clean strategy to reduce the model optimization deviation on clean dataset caused by the reduction of valid categories in each K-class hyperplane. Extensive experiments demonstrate that the proposed SKH easily achieves new state-of-the-art on IJB-B and IJB-C by encouraging noise-robust representation learning.