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Collaborative Learning With a Multi-Branch Framework for Feature Enhancement

Feature representation is highly important for many computer vision tasks. A broad range of prior studies have been proposed to strengthen representation ability of architectures via built-in blocks. However, during the forward propagation, the reduction in feature map scales still leads to the lack of representation ability. In this paper, we focus on boosting the representational power of a convolutional network by the multi-branch framework that we term the BranchNet. Each branch is directly supervised by label information to enrich the hierarchy features in BranchNet. Based on this framework, we further propose a collaborative learning loss and a soft target loss to transfer knowledge from deeper layers to shallow layers. BranchNet is an efficient training framework without extra parameters introduced in inference and can be integrated in existing networks, e.g., VGG, ResNet, and DenseNet. We evaluate BranchNet on all of these models and find that our method outperforms the baseline models on the widely-used CIFAR and ImageNet datasets. In particular, on the CIFAR-100 dataset, the classification error of ResNet-164 with BranchNet decreases by 4.51 percent. We also conduct experiments on the representative computer vision tasks of instance segmentation and class activation mapping, further verifying the superiority of BranchNet over the baseline models. Models and code are available at https://github.com/zyyupup/BranchNet/.