2014 NetworkInNetwork

• (Lin et al., 2014) ⇒ Min Lin, Qiang Chen, and Shuicheng Yan. (2013). “Network In Network.” In: Proceedings of International Conference on Learning Representations (ICLR 2014).

Subject Headings: Network-In-Network (NIN); Convolutional Neural Network; MLP Convolution Layer.

Notes

• This paper was initially submitted in 2013 but published in 2013.

Cited By

• Google Scholar: 2,702 Citations
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Quotes

Abstract

We propose a novel deep network structure called "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. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking multiple of the above described structure. With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers. We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 NetworkInNetwork	Shuicheng Yan Min Lin Qiang Chen			Network In Network						2013