2018/10/23 by Zhengsu Chen Jianwei Niu Qi Tian, Tian, Zhengsu Chen Jianwei Niu Qi · 1 citation
Computer Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Medical Image Segmentation Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1810.09849
arxiv created 2018/10/23 · openalex publication_date 2018/10/23 · arxiv updated 2018/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Using a large number of parameters , deep neural networks have achieved remarkable performance on computer vison and natural language processing tasks. However the networks usually suffer from overfitting by using too much parameters. Dropout is a widely use method to deal with overfitting. Although dropout can significantly regularize densely connected layers in neural networks, it leads to suboptimal results when using for convolutional layers. To track this problem, we propose DropFilter, a new dropout method for convolutional layers. DropFilter randomly suppresses the outputs of some filters. Because it is observed that co-adaptions are more likely to occurs inter filters rather than intra filters in convolutional layers. Using DropFilter, we remarkably improve the performance of convolutional networks on CIFAR and ImageNet.