論文筆記 | Large-Margin Softmax Loss for Convolutional Neural Networks
出處：ICML 2016 論文：http://proceedings.mlr.press/v48/liud16.pdf 代碼：https://github.com/luoyetx/mx-lsoftmax 摘要： 交叉熵損失與softmax一起可以說是卷積神經網絡（CNN）中最常用的監督組件之一。 儘管該組件具有簡單性，流行性和出色的性能，但並未明確鼓勵對功能進行辨別性學習。 本文提出了一個廣義的l
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相關文章
- 1. Large-Margin Softmax Loss for Convolutional Neural Networks
- 2. Learning Convolutional Neural Networks for Graphs論文筆記
- 3. 【Fast Algorithms for Convolutional Neural Networks】論文筆記
- 4. softmax變種或增強1：Large-Margin Softmax Loss for Convolutional Neural Networks
- 5. Diffusion-Convolutional Neural Networks論文筆記
- 6. 【論文筆記】(HEVC)《Multi-stage Attention Convolutional Neural Networks for HEVC In-Loop Filtering》
- 7. 《Convolutional Neural Networks for Sentence Classification》論文研讀
- 8. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》論文閱讀筆記
- 9. [論文筆記]Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
- 10. 論文筆記：Recurrent Convolutional Neural Networks for Text Classification