Silhouette Loss: Differentiable Global Structure Learning for Deep Representations

This paper introduces Soft Silhouette Loss, a novel differentiable objective for deep learning, inspired by the classical silhouette coefficient. It aims to learn discriminative representations by enforcing intra-class compactness and inter-class separation more efficiently than existing metric learning approaches.