Research map / Label-efficient and robust learning / Noisy labels
Active Negative Loss Functions for Learning with 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
- Normalized Loss Functions for Deep Learning with Noisy Labels (ICML 2020 · similarity 0.94)
- Asymmetric Loss Functions for Learning with Noisy Labels (ICML 2021 · similarity 0.90)
- Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning (AAAI 2025 · similarity 0.87)
- $\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise (NeurIPS 2024 · similarity 0.86)
- Mitigating Label Noise through Data Ambiguation (AAAI 2024 · similarity 0.83)
- Robust Loss Functions for Training Decision Trees with Noisy Labels (AAAI 2024 · similarity 0.83)
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.
