Deep Self-Learning From Noisy Labels
This work addresses the challenge of noisy labels in real-world datasets for machine learning practitioners, offering a robust solution that is less constrained than previous methods.
The paper tackles the problem of training convolutional neural networks from datasets with noisy labels, which typically degrades performance, and presents a deep self-learning framework that outperforms existing methods on benchmarks like Clothing1M and Food101-N without needing extra supervision or assumptions about noise distribution.
ConvNets achieve good results when training from clean data, but learning from noisy labels significantly degrades performances and remains challenging. Unlike previous works constrained by many conditions, making them infeasible to real noisy cases, this work presents a novel deep self-learning framework to train a robust network on the real noisy datasets without extra supervision. The proposed approach has several appealing benefits. (1) Different from most existing work, it does not rely on any assumption on the distribution of the noisy labels, making it robust to real noises. (2) It does not need extra clean supervision or accessorial network to help training. (3) A self-learning framework is proposed to train the network in an iterative end-to-end manner, which is effective and efficient. Extensive experiments in challenging benchmarks such as Clothing1M and Food101-N show that our approach outperforms its counterparts in all empirical settings.