when-does-label-smoothing-help

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when-does-label-smoothing-help

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Unofficial PyTorch implementation of penultimate layer visualization in the paper "When Does Label Smoothing Help?"

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# When Does Label Smoothing Help? - Visualization

Unofficial PyTorch implementation of penultimate layer visualization in the paper [*When Does Label Smoothing Help?*](https://arxiv.org/pdf/1906.02629.pdf)

## Requirements

```
pytorch
scikit-learn
matplotlib
tensorboardX
```

## Result

I trained ResNet-18 on CIFAR-10 dataset, with and without label smoothing. All train set is used for training, and test set is used for validation. The trained models achieved 94.9% (without label smoothing) and 95.1% (with label smoothing) on test set.

Below is visualizations of penultimate layer of each models. (Note: this implementation of visualization may be incorrect.)

When label smoothing is applied, you can see that *"the clusters are much tighter, because label smoothing encourages that each example in training set to be equidistant from all the other class's templates."*

### CIFAR-10 / ResNet-18 Without Label Smoothing

![without label smoothing - train](https://raw.githubusercontent.com/LuoJiaji/when-does-label-smoothing-help/master/visualization/ce-train.jpg)

without label smoothing - train

![without label smoothing - test](https://raw.githubusercontent.com/LuoJiaji/when-does-label-smoothing-help/master/visualization/ce.jpg)

without label smoothing - test

### CIFAR-10 / ResNet-18 With Label Smoothing (alpha=0.1)

![with lael smoothing - train](https://raw.githubusercontent.com/LuoJiaji/when-does-label-smoothing-help/master/visualization/ls-0.1-train.jpg)

with label smoothing - train

![with label smoothing - test](https://raw.githubusercontent.com/LuoJiaji/when-does-label-smoothing-help/master/visualization/ls-0.1.jpg)

with label smoothing - test

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