Improved Regularization of Convolutional Neural Networks with Cutout

15 Aug 2017Terrance DeVriesGraham W. Taylor

Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Domain Generalization ImageNet-A Cutout (ResNet-50) Top-1 accuracy % 4.4 # 4
Semi-Supervised Image Classification STL-10 CutOut Accuracy 87.26 # 3
Image Classification STL-10 Cutout Percentage correct 87.26 # 11
Image Classification SVHN Cutout Percentage error 1.30 # 4

Methods used in the Paper