Papers
1–3 of 3Research Paper·Mar 13, 2026
Maximizing Incremental Information Entropy for Contrastive Learning
Contrastive learning has achieved remarkable success in self-supervised representation learning, often guided by information-theoretic objectives such as mutual information maximization. Motivated by ...
7.0 viability
Research Paper·Mar 5, 2026
Guiding Diffusion-based Reconstruction with Contrastive Signals for Balanced Visual Representation
The limited understanding capacity of the visual encoder in Contrastive Language-Image Pre-training (CLIP) has become a key bottleneck for downstream performance. This capacity includes both Discrimin...
6.0 viability
Research Paper·Mar 13, 2026
Asymptotic and Finite-Time Guarantees for Langevin-Based Temperature Annealing in InfoNCE
The InfoNCE loss in contrastive learning depends critically on a temperature parameter, yet its dynamics under fixed versus annealed schedules remain poorly understood. We provide a theoretical analys...
2.0 viability