# Noise-Tolerant Paradigm for Training Face Recognition CNNs

## TL;DR

This paper introduces a noise-tolerant training paradigm for face recognition CNNs that leverages the distribution of angular margins to weight samples, enabling effective training on large-scale noisy datasets without prior noise knowledge.

## Contribution

The novel paradigm uses angular margin distribution analysis to implicitly identify clean samples, improving CNN training robustness on noisy face recognition datasets.

## Key findings

- Effective training on large-scale noisy datasets
- Improved face recognition accuracy
- No prior noise knowledge required

## Abstract

Benefit from large-scale training datasets, deep Convolutional Neural Networks(CNNs) have achieved impressive results in face recognition(FR). However, tremendous scale of datasets inevitably lead to noisy data, which obviously reduce the performance of the trained CNN models. Kicking out wrong labels from large-scale FR datasets is still very expensive, although some cleaning approaches are proposed. According to the analysis of the whole process of training CNN models supervised by angular margin based loss(AM-Loss) functions, we find that the $\theta$ distribution of training samples implicitly reflects their probability of being clean. Thus, we propose a novel training paradigm that employs the idea of weighting samples based on the above probability. Without any prior knowledge of noise, we can train high performance CNN models with large-scale FR datasets. Experiments demonstrate the effectiveness of our training paradigm. The codes are available at https://github.com/huangyangyu/NoiseFace.