2020/02/07 by Liyan Chen, Chen, Liyan, Mordohai, Philippos +2 · 2 citations
Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.2002.02997
openalex publication_date 2020/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Dropout as a common regularizer to prevent overfitting in deep neural networks has been less effective in convolutional layers than in fully connected layers. This is because Dropout drops features randomly, without considering local structure. When features are spatially correlated, as in the case of convolutional layers, information from the dropped features can still propagate to subsequent layers via neighboring features. To address this problem, 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 leverage the structure in the outputs of convolutional layers and introduce a novel structured regularization method named DropCluster. Our approach clusters features in convolutional layers, and drops the resulting clusters randomly during training iterations. Experiments on CIFAR-10/100, SVHN, and APPA-REAL datasets demonstrate that our approach is effective and controls overfitting better than other approaches.