Research map / Label-efficient and robust learning / Knowledge distillation
Learning From Biased Soft 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
- Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective (ICLR 2021 · similarity 0.91)
- A Label is Worth A Thousand Images in Dataset Distillation (NeurIPS 2024 · similarity 0.89)
- SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning (ICLR 2023 · similarity 0.88)
- Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical (ICML 2024 · similarity 0.87)
- Language-Assisted Debiasing and Smoothing for Foundation Model-Based Semi-Supervised Learning (CVPR 2025 · similarity 0.86)
- Aligned Objective for Soft-Pseudo-Label Generation in Supervised Learning (ICML 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.