Learning What to Preserve: Target-Directed Information Bottleneck for Infrared Small Target Detection Under Degradation

Information preservation is essential for infrared small target detection, but indiscriminate preservation becomes a liability when degradation dominates the input. Conventional encoders make retention an implicit consequence of task optimization, whereas invertible encoders make information recoverable without deciding what is worth retaining. This work introduces Target-Directed Information Bottleneck (TDIB), which unifies selective information retention with inference-aligned detection. Target-Directed Selective Retention (TDSR) integrates a frozen invertible prefix and a variational bottleneck, confining information loss to the bottleneck and making its cost measurable through the fixed inverse. Its spatially weighted KL rate drives information compression, while localized inverse reconstruction protects fragile target evidence without requiring clean references. Inference-Aligned Ranking (IAR) further turns posterior variation into supervision for reliable detection, encouraging mean–sample prediction consistency and ranking candidates by localization quality discounted by posterior drift. Comparisons and ablations on the proposed IR-DegradeX benchmark show that TDIB consistently improves detection accuracy, while representation analyses confirm that it preserves target evidence and stabilizes predictions. These findings point to a principled pathway toward target-directed representation learning for degraded visual perception.