論文筆記系列-iCaRL： Incremental Classifier and Representation Learning
導言 傳統的神經網絡都是基於固定的數據集進行訓練學習的，一旦有新的，不同分佈的數據進來，一般而言需要重新訓練整個網絡，這樣費時費力，而且在實際應用場景中也不適用，所以增量學習應運而生。 增量學習主要旨在解決災難性遺忘(Catastrophic-forgetting) 問題,本文將要介紹的《iCaRL： Incremental Classifier and Representation Learni
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- 1. iCaRL Incremental Classifier and Representation Learning 翻譯
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相關文章
- 1. iCaRL Incremental Classifier and Representation Learning 翻譯
- 2. 讀書筆記-增量學習-iCaRL: Incremental Classifier and Representation Learning
- 3. iCaRL：Incremental Classifier and Representation Learning論文閱讀
- 4. 《Decoupling Representation and Classifier》筆記
- 5. 【論文筆記】Representation Learning by Learning to Count
- 6. 論文筆記: Inductive Representation Learning on Large Graphs
- 7. Inductive Representation Learning on Large Graphs論文筆記
- 8. 《Incremental Classifier Learning with Generative Adversarial Networks》 閱讀筆記
- 9. 論文筆記：Representation Learning with Contrastive Predictive Coding
- 10. 論文筆記：Inductive Representation Learning on Large Graphs