AINeutralarXiv – CS AI · 10h ago6/10
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Silhouette Loss: Differentiable Global Structure Learning for Deep Representations
Researchers introduce Soft Silhouette Loss, a novel machine learning objective that improves deep neural network representations by enforcing intra-class compactness and inter-class separation. The lightweight differentiable loss outperforms cross-entropy and supervised contrastive learning when combined, achieving 39.08% top-1 accuracy compared to 37.85% for existing methods while reducing computational overhead.