LossTransform: Reformulating the Loss Function for Contrastive Learning

Contrastive learning improves model performance by differentiating between positive and negative sample pairs. However, its application is primarily confined to classification tasks, facing challenges with complex recognition tasks such as object detection and segmentation due to its limited capacity to capture spatial relationships and fine-grained features. To address this limitation, we propose LossTransform, a novel approach that redefines positive sample pairs and establishes a novel contrastive loss paradigm. LossTransform advances contrastive learning to the instance level, departing from the traditional sample level. Empirical evaluations on ImageNet, CIFAR, and object detection benchmarks indicate that LossTransform improves accuracy by +2.73% on CIFAR, +2.52% on ImageNet, and up to +5.2% in average precision on detection tasks, while maintaining efficiency. These results illustrate that LossTransform is compatible with large-scale training pipelines and exhibits robust performance across diverse and complex datasets. By optimizing model performance and significantly reducing training time, this research enables more efficient and accessible solutions for societal applications.