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Dropout as a regularizer in deep neural networks has been less effective in convolutional layers than in fully connected layers. This is due to the fact that dropout drops features randomly. When features are spatially correlated as in the case of convolutional layers, information about the dropped pixels can still propagate to the next layers via neighboring pixels. In order to address this problem, more structured forms of dropout have been proposed. A drawback of these methods is that they do not adapt to the data. In this work, we introduce a novel structured regularization for convolutional layers, which we call DropCluster. Our regularizer relies on data-driven structure. It finds clusters of correlated features in convolutional layer outputs and drops the clusters randomly at each iteration. The clusters are learned and updated during model training so that they adapt both to the data and to the model weights. Our experiments on the ResNet-50 architecture demonstrate that our approach achieves better performance than DropBlock or other existing structured dropout variants. We also demonstrate the robustness of our approach when the size of training data is limited and when there is corruption in the data at test time., Comment: 11 pages, 10 figures, under review

The availability and easy access to digital communication increase the risk of copyrighted material piracy. In order to detect illegal use or distribution of data, digital watermarking has been proposed as a suitable tool. It protects the copyright of digital content by embedding imperceptible information into the data in the presence of an adversary. The goal of the adversary is to remove the copyrighted content of the data. Therefore, an efficient watermarking framework must be robust to multiple image-processing operations known as attacks that can alter embedded copyright information. Another line of research \textit{adversarial machine learning} also tackles with similar problems to guarantee robustness to imperceptible perturbations of the input. In this work, we propose to apply robust optimization from adversarial machine learning to improve the robustness of a CNN-based watermarking framework. Our experimental results on the COCO dataset show that the robustness of a watermarking framework can be improved by utilizing robust optimization in training., Comment: 5 pages, 1 figure, Machine Learning with Guarantees workshop at NeurIPS 2019