Research map / Label-efficient and robust learning / Knowledge distillation
Revisiting Cross-Architecture Distillation: Adaptive Dual-Teacher Transfer for Lightweight Video Models
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
- ScaleKD: Strong Vision Transformers Could Be Excellent Teachers (NeurIPS 2024 · similarity 0.90)
- Vision Transformers with Self-Distilled Registers (NeurIPS 2025 · similarity 0.87)
- Attention Transfer Is Not Universally Effective for Vision Transformers (NeurIPS 2026 · similarity 0.86)
- Progressively Knowledge Distillation via Re-parameterizing Diffusion Reverse Process (AAAI 2024 · similarity 0.86)
- MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders (CVPR 2025 · similarity 0.86)
- No Head Left Behind – Multi-Head Alignment Distillation for Transformers (AAAI 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.
