Research map / Label-efficient and robust learning / Noisy labels
Robust early-learning: Hindering the memorization of noisy labels
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Early-Learning Regularization Prevents Memorization of Noisy Labels (NeurIPS 2020 · similarity 0.92)
- Understanding and Improving Early Stopping for Learning with Noisy Labels (NeurIPS 2021 · similarity 0.92)
- Early Stopping Against Label Noise Without Validation Data (ICLR 2024 · similarity 0.89)
- Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization (NeurIPS 2025 · similarity 0.89)
- On the Role of Label Noise in the Feature Learning Process (ICML 2025 · similarity 0.88)
- How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime? (NeurIPS 2025 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
