閱讀理解《Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph D》
文章摘要提出要解決Video representation learning問題現存的方法存在兩個侷限： 1）僅僅侷限在一個任務，忽視了不同任務特徵的補充，因此導致了結果的次優。 2）高額的計算和記憶消耗阻礙了在現實情境的應用。 而本篇文章提出了一個基於圖形的提取框架來處理這些問題： 1）提出了 logits graph、 representation graph 來從多個自我監督任務中
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相關文章
- 1. Graph Few-shot learning via Knowledge Transfer
- 2. Transfer Learning for Item Recommendations and Knowledge Graph Completion
- 3. NASE Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search 閱讀筆記
- 4. A Gift from Knowledge Distillation: Fast Optimization,Network Minimization and Transfer Learning論文初讀
- 5. 遷移學習論文閱讀：Transfer Learning via Learning to Transfer
- 6. (13)[ICLR16] Net2Net: ACCELERATING LEARNING VIA KNOWLEDGE TRANSFER
- 7. Unifying Task-oriented Knowledge Graph Learning and Recommendation
- 8. Graphonomy: Universal Human Parsing via Graph Transfer Learning
- 9. transfer learning
- 10. 《STAR: A Schema-Guided Dialog Dataset for Transfer Learning》論文閱讀