(2013. 12) Network In Network
Submitted on 2013. 12
Min Lin, Qiang Chen and Shuicheng Yan
Simple Summary
"Network In Network" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field.
Mlpconv layer with “micronetwork” within each conv layer to compute more abstract features for local patches.
Micronetwork uses multilayer perceptron (FC, i.e. 1x1 conv layers)
Precursor to GoogLeNet and ResNet “bottleneck” layers.
Philosophical inspiration for GoogLeNet.
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