Modern convolutional neural networks (CNNs) have massive identical convolution blocks, and, hence, recursive sharing of parameters across these blocks has been proposed to reduce the amount of parameters. However, naive sharing of parameters poses many challenges such as limited representational power and the vanishing/exploding gradients problem of recursively shared parameters. In this paper, we present a recursive convolution block design and training method, in which a recursively shareable part, or a filter basis, is separated and learned while effectively avoiding the vanishing/exploding gradients problem during training. We show that the unwieldy vanishing/exploding gradients problem can be controlled by enforcing the elements of the filter basis orthonormal, and empirically demonstrate that the proposed orthogonality regularization improves the flow of gradients during training. Experimental results on image classification and object detection show that our approach, unlike previous parameter-sharing approaches, does not trade performance to save parameters and consistently outperforms overparameterized counterpart networks. This superior performance demonstrates that the proposed recursive convolution block design and the orthogonality regularization not only prevent performance degradation, but also consistently improve the representation capability while a significant amount of parameters are recursively shared.
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
Structural Kolmogorov-Arnold Convolutions: Learnable Function on the Values or the Filter Shape as Parameter-Efficient Alternative to Per-Edge Convolutional KANs
Istituto Italiano di Tecnologia（意大利技术研究院）；Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa（热那亚大学信息学、生物工程、机器人与系统工程系）；Department of Theoretical and Applied Sciences (DISTA), eCampus University（eCampus大学理论与应用科学系）；Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V. N. Karazin Kharkiv National University（哈尔科夫国立大学计算机科学与人工智能学院智能软件系统与技术系）；Department of Information Engineering, Marche Polytechnic University（马尔凯理工大学信息工程系）
Learning Task-preferred Inference Routes for Gradient De-conflict in Multi-output DNNs
Chinese Academy of Sciences（中国科学院）；Beijing Institute of Basic Medical Sciences（北京基础医学研究所）；College of Intelligence Science and Technology（智能科学与技术学院）；National University of Defense Technology（国防科技大学）
RoutingGAN: Routing Age Progression and Regression with Disentangled Learning
1 Shanghai Key Lab of Intelligent Information Processing, School of Computer Science 2 Institute of Science；Technology for Brain-inspired Intelligence \ University, Shanghai 200433, China
Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook
Khalifa University of Science and Technology（哈利法科技大学）；ETH Zürich（苏黎世联邦理工学院）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Linköping University（林雪平大学）；Czech Technical University（捷克理工大学）
