Regularizing Class-wise Predictions via Self-knowledge Distillation
解決的問題是圖像分類模型的過擬合，做法利用類class-wise 正則化項。思想是比如上圖，這個思想簡單的作爲KL散度中不動的一方，下面這個背景複雜的作爲動的一方，在類別預測的概率分佈上進行蒸餾，過濾掉黑暗知識。一個模型，兩個不同的樣本但是類別是一致的，帶有標籤。
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相關文章
- 1. Knowledge Distillation via Route Constrained Optimization
- 2. Awesome Knowledge-Distillation
- 3. Understanding black-box predictions via influence functions_1703.04730_icml_best論文理解
- 4. （2017ICML Bestpaper）Understanding Black-box Predictions via Influence Functions 筆記
- 5. 論文筆記understanding black-box predictions via influence functions
- 6. 論文淺嘗 | Understanding Black-box Predictions via Influence Func
- 7. Knowledge Distillation
- 8. Fast and Accurate Single Image Super-Resolution via Information Distillation Network
- 9. PaperNote-Fast and Accurate Single Image Super-Resolution via Information Distillation Network
- 10. 【distill.&transfer】Deep Face Recognition Model Compression via Knowledge Transfer and Distillation