Research map / Label-efficient and robust learning / Knowledge distillation
Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift
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
- Rethinking Dataset Distillation: Hard Truths about Soft Labels (CVPR 2026 · similarity 0.91)
- A Label is Worth A Thousand Images in Dataset Distillation (NeurIPS 2024 · similarity 0.87)
- Learning From Biased Soft Labels (NeurIPS 2023 · similarity 0.87)
- GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost (ICLR 2025 · similarity 0.87)
- All Labels Are Not Created Equal: Enhancing Semi-Supervision via Label Grouping and Co-Training (CVPR 2021 · similarity 0.87)
- Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation? (NeurIPS 2024 · similarity 0.86)
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.
