【論文閱讀】Gradient Centralization: A New Optimization Technique for Deep Neural Networks
香港理工&阿里達摩院新作，操作優雅，一行代碼嵌入優化器提升性能！ ArXiv 鏈接：https://arxiv.org/abs/2004.01461 Github 鏈接：https://github.com/Yonghongwei/Gradient-Centralization 一、摘要 優化技術對有效地訓練深度神經網絡 (DNN) 而言具有重要意義。結果表明，利用一階和二階統計量 (如均值
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相關文章
- 1. 【論文閱讀】Gradient Centralization: A New Optimization Technique for Deep Neural Networks
- 2. Gradient Centralization: A New Optimization Technique for Deep Neural Networks（講解）
- 3. 論文閱讀：Deep Neural Networks for Object Detection
- 4. [論文解讀] Concolic Testing for Deep Neural Networks
- 5. 【論文閱讀】[2009.00278v2]Scaling Up Deep Neural Network Optimization for Edge Inference
- 6. 論文閱讀-Fast Algorithms for Convolutional Neural Networks
- 7. 論文閱讀：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
- 8. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 9. 論文閱讀--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 10. [論文閱讀]ResNetXt|Aggregated Residual Transformations for Deep Neural Networks