(13)[ICLR16] Net2Net: ACCELERATING LEARNING VIA KNOWLEDGE TRANSFER
計劃完成深度學習入門的126篇論文第十三篇，UW的陳天奇和Goodfellow合著針對知識遷移的加速學習Net2Net。 ABSTRACT&INTRODUCTION 摘要 我們介紹了將存儲在一個神經網絡中的信息快速傳輸到另一個神經網絡的技術。主要目的是加速訓練一個更大的神經網絡。在實際的工作流程中，人們經常在實驗和設計過程中訓練許多不同的神經網絡。每個新模型都是從零開始訓練的，所以這是一個浪費時間
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相關文章
- 1. (13)[ICLR16] Net2Net: ACCELERATING LEARNING VIA KNOWLEDGE TRANSFER
- 2. Graph Few-shot learning via Knowledge Transfer
- 3. Transfer Learning[論文合集]
- 4. Paper Note --- Transfer Learning via Dimensionality Reduction
- 5. Transfer Learning for Item Recommendations and Knowledge Graph Completion
- 6. 遷移學習論文閱讀：Transfer Learning via Learning to Transfer
- 7. 【distill.&transfer】Deep Face Recognition Model Compression via Knowledge Transfer and Distillation
- 8. transfer learning
- 9. Graphonomy: Universal Human Parsing via Graph Transfer Learning
- 10. 閱讀理解《Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph D》