Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models (2402.03749v1)

Abstract: Recent advancements in LLMs have sparked interest in their extraordinary and near-superhuman capabilities, leading researchers to explore methods for evaluating and optimizing these abilities, which is called superalignment. In this context, our paper delves into the realm of vision foundation models, focusing on the concept of weak-to-strong generalization, which involves using a weaker model to supervise a stronger one, aiming to enhance the latter's capabilities beyond the former's limits. We introduce a novel and adaptively adjustable loss function for weak-to-strong supervision. Our comprehensive experiments span various scenarios, including few-shot learning, transfer learning, noisy label learning, and common knowledge distillation settings. The results are striking: our approach not only exceeds the performance benchmarks set by strong-to-strong generalization but also surpasses the outcomes of fine-tuning strong models with whole datasets. This compelling evidence underscores the significant potential of weak-to-strong generalization, showcasing its capability to substantially elevate the performance of vision foundation models. The code is available at https://github.com/ggjy/vision_weak_to_strong.

Background and Objectives

In the continually evolving landscape of AI and deep learning, superalignment has surfaced as a notable concept, particularly concerning vision foundation models. Weak-to-Strong Generalization (WSG) is a method that ventures into the intriguing notion of using weaker models to supervise stronger ones, aiming to bolster the capabilities of the latter. This paper introduces an innovative and adaptable loss function to enhance weak-to-strong supervision, setting new benchmarks in various vision tasks.

Methodology and Approach

The central innovation of this paper is the introduction of an adaptive confidence distillation method, which dynamically adjusts according to a model's confidence in its predictions. The confidence is derived from the discrepancy between the soft and hard labels produced by the model, facilitating a nuanced balance in the learning process. The assessment encompasses various scenarios, including few-shot learning, transfer learning, noisy label learning, and conventional knowledge distillation settings, illustrating the method's versatility and effectiveness across different modalities.

Experimental Results

The empirical results presented in this paper are quite compelling, with the proposed method, AdaptConf, outperforming existing knowledge distillation methods across most evaluated tasks. For instance, significant performance improvements are reported in the CIFAR-100 image classification tasks, with consistent superiority demonstrated across different architectural configurations. Additionally, the method achieves improvement in few-shot learning accuracy and shows notable robustness in transferring a vision transformer trained with self-supervision to labeled datasets. Notably, the paper presents robust performance under conditions of noisy data, where the proposed method mitigates the impact of false labels better than other approaches.

Implications and Conclusion

By demonstrating the validity of weak-to-strong generalization, the research not only affirms its potential in the visual domain but also contributes to the broader concept of superalignment across AI disciplines. It implies that models with hefty computational capabilities can substantially benefit from the supervision provided by less capable models. The work opens up new avenues for AI advancement, enabling superhuman AI capabilities while still drawing on human-level expertise. The release of the associated codebase provides a platform for further exploration and refinement in the field.