arXiv:2602.06883v3 Announce Type: replace Abstract: The smoothness of the transformer architecture has been extensively studied in the context of generalization, training stability, and adversarial robustness. However, its role in transfer learning remains poorly understood. In this paper, we analyze the ability of vision transformer components to adapt their outputs to changes in inputs, or, in other words, their \emph{plasticity}. Defined as an average rate of change, it captures the sensitivity to input perturbation; in particular, a high plasticity implies a low smoothness. Our theoretical
The paper provides new theoretical insights into Vision Transformer finetuning, which is a critical area for improving AI model performance and efficiency, building on extensive prior research into transformer architecture smoothness.
Understanding the 'plasticity' of Vision Transformers can lead to more robust, efficient, and adaptable AI models, directly impacting the development and deployment of advanced AI applications across various industries.
The focus shifts towards understanding and potentially leveraging non-smooth components in Vision Transformers to enhance transfer learning capabilities, challenging previous assumptions about optimal model smoothness.
- · AI researchers
- · Machine learning developers
- · Industries relying on computer vision
- · Hardware manufacturers for AI
- · Developers using less optimized finetuning strategies
- · Companies with less adaptive AI infrastructure
Improved finetuning techniques will lead to more effective and versatile vision transformers for specific tasks.
This could accelerate the deployment of advanced AI in fields like autonomous driving, medical imaging, and robotics due to better model adaptation.
Enhanced model plasticity and transfer learning capabilities might reduce the need for massive datasets for new tasks, lowering compute and data requirements for AI development.
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Read at arXiv cs.LG