The paper "Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models" explores the concept of weak-to-strong generalization in vision foundation models, aiming to enhance the capabilities of stronger models using weaker models. The authors introduce an adaptive confidence loss function, AdaptConf, which dynamically adjusts the balance between learning from the weak model and relying on the strong model's own predictions. This approach is evaluated across various tasks, including few-shot learning, transfer learning, noisy label learning, and common knowledge distillation settings. The results show that AdaptConf outperforms existing methods, both in terms of performance benchmarks and fine-tuning strong models with whole datasets. The paper highlights the significant potential of weak-to-strong generalization in advancing the performance of vision foundation models. The code for the proposed method is available at https://github.com/ggj/vision_weak_to_strong.The paper "Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models" explores the concept of weak-to-strong generalization in vision foundation models, aiming to enhance the capabilities of stronger models using weaker models. The authors introduce an adaptive confidence loss function, AdaptConf, which dynamically adjusts the balance between learning from the weak model and relying on the strong model's own predictions. This approach is evaluated across various tasks, including few-shot learning, transfer learning, noisy label learning, and common knowledge distillation settings. The results show that AdaptConf outperforms existing methods, both in terms of performance benchmarks and fine-tuning strong models with whole datasets. The paper highlights the significant potential of weak-to-strong generalization in advancing the performance of vision foundation models. The code for the proposed method is available at https://github.com/ggj/vision_weak_to_strong.