Putting An End to End-to-End: Gradient-Isolated Learning of Representations 筆記
Putting An End to End-to-End: Gradient-Isolated Learning of Representations 1. 論文講了什麼/主要貢獻是什麼 文章提出了Greedy InfoMax算法，該算法在貪婪自監督訓練的情況下，仍能較好地完成音頻和圖像分類任務。 神經網絡能夠進行異步、解耦訓練，允許在大於內存的輸入數據上對任意深度的網絡進行訓練。 文中證明了互信
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相關文章
- 1. End-to-End Learning of Deep Visual Representations for Image Retrieval
- 2. 【論文筆記】DeepOrigin: End-to-End Deep Learning for Detection of New Malware Families
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