Performance enhancement research on channel pruning and semantic decoupling in self-supervised learning networks

Abstract Self-supervised learning, due to its label-independent nature, has been widely applied in the field of deep learning. However, existing methods still face limitations in semantic information extraction and model compression within the convolutional neural network channels. This article proposes a method combining self-supervised learning distillation and pruning to optimize channel importance identification and model performance. By leveraging self-supervised learning to capture channel features, the method identifies critical channels and deactivates redundant ones, effectively decoupling semantic information and significantly improving accuracy and efficiency in subclassification tasks. Specifically, the proposed approach evaluates channel importance through a gated mechanism, constructing task-specific efficient subnetworks while employing structured pruning to reduce computational resource consumption. Experiments conducted on the CIFAR-10 dataset validate the proposed method, demonstrating its ability to significantly reduce model parameters and improve subclassification accuracy while maintaining high overall accuracy.