《Incremental Classifier Learning with Generative Adversarial Networks》 閱讀筆記
原文鏈接： Incremental Classifier Learning with Generative Adversarial Networks 本文主要是解決增量學習中災難性遺忘的問題，文中指出災難性遺忘主要是因爲過去的數據在訓練時得不到。通常的方法是先保留一部分過去類別的樣本，然後配合蒸餾正則化來平衡新舊類別之間的關係。但是這些方法主要有四個問題： 1）Loss 函數的設計對分類器來說都不
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相關文章
- 1. 【閱讀筆記】Generative Adversarial Nets
- 2. BEGAN: Boundary Equilibrium Generative Adversarial Networks閱讀筆記
- 3. Generating Adversarial Examples with Adversarial Networks閱讀筆記
- 4. 論文閱讀筆記《Generative Adversarial Residual Pairwise Networks for One Shot Learning 》
- 5. 論文閱讀——《Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks》
- 6. DCGAN：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- 7. UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
- 8. Generative Adversarial Networks: An Overview文獻閱讀筆記
- 9. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 10. [解讀] Coupled Generative Adversarial Networks