論文學習 Branched Multi-Task Networks: Deciding What Layers To Share
1. 多任務學習網絡 隨着ICCV2017最佳論文授予kaiming大神的Mask-RCNN，多任務網絡成爲了新的研究熱點。Mask-RCNN基於目標檢測框架，在RoI pooling層後設計兩個並行的子網絡：一個分支是原始的目標檢測分支，即判斷物體類別和迴歸邊框，另一個分支實現其他任務，如實例分割或人體關鍵點檢測等，多任務同時學習提高了各自任務的性能（相比單獨訓練某個任務）。無獨有偶，CVPR2
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