Identity Mappings In Deep Residual Networks
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RefineNet: Multi-Path Refinement Networks for High ...
openaccess.thecvf.comploy residual connections [24] with identity mappings [25], such that gradients can be directly propagated through short-range and long-range residual connections allowing for both effective and efficient end-to-end training. 3. We propose a new network component we call chained residual pooling which is able to capture back-
Deep Residual Learning for Image Recognition
arxiv.orgidentity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. The formulation of F(x)+x can be realized by feedfor-ward neural networks with “shortcut connections” (Fig.2). Shortcut connections [2,34,49] are those skipping one or more layers.
Deep Residual Learning for Image Recognition
openaccess.thecvf.comidentity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. The formulation of F(x)+xcan be realized by feedfor-ward neural networks with “shortcut connections” (Fig. 2). Shortcut connections [2, 33, 48] are those skipping one or more layers.
Densely Connected Convolutional Networks - arXiv
arxiv.orgNetworks [34] and Residual Networks (ResNets) [11] have surpassed the 100-layer barrier. As CNNs become increasingly deep, a new research problem emerges: as information about the input or gra-dient passes through many layers, it can vanish and “wash out” by the time it reaches the end (or beginning) of the network.