Kernel Induced Loss

Kernel-induced losses are a powerful technique used to define and optimize structured prediction tasks, particularly in scenarios involving complex data like images and text. Current research focuses on integrating these losses with deep neural networks, often employing architectures that leverage spectral filtering or operate within data-dependent subspaces to improve efficiency and gradient-based training. This approach enhances the versatility of kernel methods, leading to improved performance in various applications, including contrastive learning, few-shot learning, and semi-supervised learning, by addressing challenges like label noise and class imbalance. The resulting models demonstrate improved robustness and generalization capabilities across diverse datasets.