Vision Superalignment: Weak-to-Strong Generalization for Vision
  Foundation Models
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.
65c2f1bc69ac028bb50cf209
Jianyuan Guo
65c2f1bc69ac028bb50cf20a
Hanting Chen
65c2f1bc69ac028bb50cf20b
Chengcheng Wang
65c2f1bc69ac028bb50cf20c
Kai Han
claimed_verified
2024-03-07T09:16:11.910Z
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/avatars/022a179d14de29b9ab9d96fcc85aa264.svg
hankai
user
hankaixyz
65c2f1bc69ac028bb50cf20d
Chang Xu
65c2f1bc69ac028bb50cf20e
Yunhe Wang
Vision Superalignment: Weak-to-Strong Generalization for Vision
  Foundation Models
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.
Weak-to-strong supervision using an adaptively adjustable loss function significantly enhances vision foundation models' performance across various learning scenarios.
weak-to-strong generalization strong-to-strong generalization few-shot learning transfer learning noisy label learning common knowledge distillation loss function