[1409.5185] Deeply-Supervised Nets

Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. We make an attempt to boost the classification performance by studying a new formulation in deep networks. Three aspects in convolutional neural networks (CNN) style architectures are being looked at: (1) transparency of the intermediate layers to the overall classification; (2) discriminativeness and robustness of learned features, especially in the early layers; (3) effectiveness in training due to the presence of the exploding and vanishing gradients. We introduce "companion objective" to the individual hidden layers, in addition to the overall objective at the output layer (a different strategy to layer-wise pre-training). We extend techniques from stochastic gradient methods to analyze our algorithm. The advantage of our method is evident and our experimental result on benchmark datasets shows significant performa

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@Maxwell_110
@Maxwell_110 少し古めだけど,Kaggle ではしばしば見かける技術 ⬇ Deep Supervision (Deeply-Supervised Nets) t.co/6rn5iUSBI5 中間層に出力を設け loss を計算し,最終層の出力の loss に正則化項として統合 これにより,中間層の表現が改善し,収束速度・精度が向上するとしている

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