Research map / Label-efficient and robust learning / Noisy labels
Resurfacing the Instance-only Dependent Label Noise Model through Loss Correction
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
- Label Noise: Ignorance Is Bliss (NeurIPS 2024 · similarity 0.90)
- Confidence Scores Make Instance-dependent Label-noise Learning Possible (ICML 2021 · similarity 0.90)
- Learning with Feature-Dependent Label Noise: A Progressive Approach (ICLR 2021 · similarity 0.89)
- Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise (AAAI 2021 · similarity 0.89)
- Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization (NeurIPS 2025 · similarity 0.87)
- Mitigating Label Noise through Data Ambiguation (AAAI 2024 · similarity 0.87)
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.
