(13)[ICLR16] Net2Net: ACCELERATING LEARNING VIA KNOWLEDGE TRANSFER
计划完成深度学习入门的126篇论文第十三篇，UW的陈天奇和Goodfellow合著针对知识迁移的加速学习Net2Net。 ABSTRACT&INTRODUCTION 摘要 我们介绍了将存储在一个神经网络中的信息快速传输到另一个神经网络的技术。主要目的是加速训练一个更大的神经网络。在实际的工作流程中，人们经常在实验和设计过程中训练许多不同的神经网络。每个新模型都是从零开始训练的，所以这是一个浪费时间
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相关文章
- 1. (13)[ICLR16] Net2Net: ACCELERATING LEARNING VIA KNOWLEDGE TRANSFER
- 2. Graph Few-shot learning via Knowledge Transfer
- 3. Transfer Learning[论文合集]
- 4. Paper Note --- Transfer Learning via Dimensionality Reduction
- 5. Transfer Learning for Item Recommendations and Knowledge Graph Completion
- 6. 迁移学习论文阅读：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. 阅读理解《Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph D》