The paper "Improved Regularization of Convolutional Neural Networks with Cutout" by Terrance DeVries and Graham W. Taylor introduces a simple yet effective regularization technique called "cutout" for convolutional neural networks (CNNs). Cutout involves randomly masking out square regions of the input during training, which helps improve the model's robustness and performance. The authors demonstrate that cutout can be easily implemented and combined with other regularization techniques, such as data augmentation and dropout, to enhance model performance. They evaluate cutout on several state-of-the-art architectures using datasets like CIFAR-10, CIFAR-100, and SVHN, achieving new state-of-the-art results with test errors of 2.56%, 15.20%, and 1.30% respectively. The method encourages the network to utilize the full context of the image, rather than relying on specific visual features, and is particularly effective in low-data and high-resolution scenarios. The paper also includes an analysis of how cutout affects feature activations, showing that it promotes the use of a wider variety of features in the network.The paper "Improved Regularization of Convolutional Neural Networks with Cutout" by Terrance DeVries and Graham W. Taylor introduces a simple yet effective regularization technique called "cutout" for convolutional neural networks (CNNs). Cutout involves randomly masking out square regions of the input during training, which helps improve the model's robustness and performance. The authors demonstrate that cutout can be easily implemented and combined with other regularization techniques, such as data augmentation and dropout, to enhance model performance. They evaluate cutout on several state-of-the-art architectures using datasets like CIFAR-10, CIFAR-100, and SVHN, achieving new state-of-the-art results with test errors of 2.56%, 15.20%, and 1.30% respectively. The method encourages the network to utilize the full context of the image, rather than relying on specific visual features, and is particularly effective in low-data and high-resolution scenarios. The paper also includes an analysis of how cutout affects feature activations, showing that it promotes the use of a wider variety of features in the network.