Reading: Cutout — Improved Regularization of Convolutional Neural Networks (Image Classification)

Regularization on Input, Improve the Accuracy of ResNet, WRN, and Shake-Shake.

Cutout applied to images from the CIFAR-10 dataset.


1. Motivation

2. Differences from Dropout

3. Experimental Results

3.1. CIFAR10, CIFAR100, SHVN

Cutout patch length with respect to validation accuracy with 95% confidence intervals (average of five runs).
Test error rates (%) on CIFAR (C10, C100) and SVHN datasets

3.2. STL-10

Test error rates on STL-10 dataset. “+” indicates standard data augmentation (mirror + crop). Results averaged over five runs on full training set.

3.3. Analysis of Cutout’s Effect on Activations

Magnitude of feature activations, sorted by descending value, and averaged over all test samples

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