Papers › Network In Network
Network In Network
Min Lin, Qiang Chen, Shuicheng Yan
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 mutiple 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.
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Code
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Code Syntology ran Syntology
7 samples harvested; 1 ran; 1 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Identification | DroneSURF | Naive Averaging (Adaface) | Rank1 | 46.87 | #5 of 6 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | Network in Network | Percentage correct | 91.2 | #195 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | NiN | Percentage correct | 64.3 | #193 of 211 | Archive leaderboard | report |
| Image Classification | MNIST | NiN | Percentage error | 0.5 | #34 of 81 | Archive leaderboard | report |
| Image Classification | SVHN | Network in Network | Percentage error | 2.35 | #34 of 62 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: 1x1 Convolution, Global Average Pooling
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