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Potential Energy based Mixture Model for Noisy Label Learning

arXiv 2405.01186 ↗First posted 2 May 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Training deep neural networks (DNNs) from noisy labels is an important and challenging task. However, most existing approaches focus on the corrupted labels and ignore the importance of inherent data structure. To bridge the gap between noisy labels and data, inspired by the concept of potential energy in physics, we propose a novel Potential Energy based Mixture Model (PEMM) for noise-labels learning. We innovate a distance-based classifier with the potential energy regularization on its class centers. Embedding our proposed classifier with existing deep learning backbones, we can have robust networks with better feature representations. They can preserve intrinsic structures from the data, resulting in a superior noisy tolerance. We conducted extensive experiments to analyze the efficiency of our proposed model on several real-world datasets. Quantitative results show that it can achieve state-of-the-art performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 31 references that are in the map. First posted on arXiv 2 May 2024.

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References · 22

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10 more
  1. 2014Training Deep Neural Networks on Noisy Labels with Bootstrappingcited by 322
  2. 2017Decoupling "when to update" from "how to update"cited by 230
  3. 2017Learning from Noisy Labels with Distillationcited by 160
  4. 2019Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networkscited by 158
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