The Role of Contrastive Loss Functions in X-CLR Contrastive loss functions are essential to self-supervised learning and multimodal AI models, serving as the mechanism by which AI learns to discern ...

The Role of Contrastive Loss Functions in X-CLR Contrastive loss functions are essential to self-supervised learning and multimodálna AI models, serving as the mechanism by which AI learns to discern ...

Furthermore, we investigate the effectiveness of various loss functions, including Cross-Entropy Loss, Contrastive Loss, and Focal Loss, in improving the robustness and accuracy of emotion recognition ...

Where τ is a scaling factor called temperature, 𝟙 is an indicator function with output values being 0 or 1, N is the number of training samples, exp (·) is the exponential function, and cos (·) is ...

Contrastive learning has gained popularity and pushes state-of-the-art performance across numerous large-scale benchmarks. In contrastive learning, the contrastive loss function plays a pivotal role ...

CLR, a novel contrastive learning method using graph-based sample relationships. This approach outperformed traditional ...

Researchers presented a study demonstrating how advanced alignment methods, such as identity preference optimization (IPO) ...

Despite the remarkable progress of self-supervised learning (SSL), how self-supervised representations generalize to out-of-distribution data remains little understood. In this paper, we study the ...

To address the problem, this paper introduces a new physics-based end-to-end deep learning approach to haze mitigation in outdoor scenes, including those in airborne images. The proposed model named ...