Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning

• URL: http://arxiv.org/abs/2306.05497v3
• Date: Mon, 27 Jan 2025 10:05:55 GMT
• Title: Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning
• Authors: Max Staats, Matthias Thamm, Bernd Rosenow,
• Abstract summary: Large annotated datasets inevitably contain noisy labels, which poses a major challenge for training deep neural networks as they easily memorize the labels. Noise-robust loss functions have emerged as a notable strategy to counteract this issue, but it remains challenging to create a robust loss function which is not susceptible to underfitting. We propose a novel method denoted as logit bias, which adds a real number $epsilon$ to the logit at the position of the correct class.
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• Abstract: Large annotated datasets inevitably contain noisy labels, which poses a major challenge for training deep neural networks as they easily memorize the labels. Noise-robust loss functions have emerged as a notable strategy to counteract this issue, but it remains challenging to create a robust loss function which is not susceptible to underfitting. Through a quantitative approach, this paper explores the limited overlap between the network output at initialization and regions of non-vanishing gradients of bounded loss functions in the initial learning phase. Using these insights, we address underfitting of several noise robust losses with a novel method denoted as logit bias, which adds a real number $\epsilon$ to the logit at the position of the correct class. The logit bias enables these losses to achieve state-of-the-art results, even on datasets like WebVision, consisting of over a million images from 1000 classes. In addition, we demonstrate that our method can be used to determine optimal parameters for several loss functions -- without having to train networks. Remarkably, our method determines the hyperparameters based on the number of classes, resulting in loss functions which require zero dataset or noise-dependent parameters.

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