Optimization of Deep Visual Models for Image Recognition Using Self-Supervised Learning

Self-supervised learning, as an important paradigm in the field of deep learning, significantly reduces the dependence on large-scale labeled data by learning effective representations from unlabeled data. This paper proposes a self-supervised visual model optimization framework based on contrastive learning, which generates multi-view samples through data augmentation strategies, extracts features using an encoder network, and employs a dual projection head architecture to achieve collaborative optimization of similarity loss and representation learning. Experiments on the CIFAR-10 dataset demonstrate that the proposed method achieves significant performance improvement in image classification tasks, with a Top-1 accuracy of 92.347%, representing an improvement of 3.826 percentage points over traditional supervised learning methods and 2.213 percentage points over the SimCLR method. Ablation experiments further verify the effectiveness of the dual projection head architecture and joint loss function, providing a new technical path for constructing label-efficient deep visual models.