title: Vision Superalignment: Weak-to-Strong Generalization for Vision
    Foundation Models
summary: Recent advancements in large language models 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.
paper_authors._id: 65c2f1bc69ac028bb50cf209
paper_authors.name: Jianyuan Guo
paper_authors._id: 65c2f1bc69ac028bb50cf20a
paper_authors.name: Hanting Chen
paper_authors._id: 65c2f1bc69ac028bb50cf20b
paper_authors.name: Chengcheng Wang
paper_authors._id: 65c2f1bc69ac028bb50cf20c
paper_authors.name: Kai Han
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-07T09:16:11.910Z
paper_authors._id: 65e52e7d27dc8aa470a640e3
paper_authors.avatarUrl: /avatars/022a179d14de29b9ab9d96fcc85aa264.svg
paper_authors.fullname: hankai
paper_authors.type: user
paper_authors.user: hankaixyz
paper_authors._id: 65c2f1bc69ac028bb50cf20d
paper_authors.name: Chang Xu
paper_authors._id: 65c2f1bc69ac028bb50cf20e
paper_authors.name: Yunhe Wang
paper_title: Vision Superalignment: Weak-to-Strong Generalization for Vision
    Foundation Models
paper_summary: Recent advancements in large language models 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.
paper_ai_keywords: weak-to-strong generalization large language models vision foundation models adaptively adjustable loss function few-shot learning transfer learning noisy label learning common knowledge distillation strong-to-strong generalization