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arXiv · 2306.06849

Mitigating Transformer Overconfidence via Lipschitz Regularization

Wenqian Ye·Yunsheng Ma·Xu Cao·Kun Tang
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Abstract

Though Transformers have achieved promising results in many computer vision tasks, they tend to be over-confident in predictions, as the standard Dot Product Self-Attention (DPSA) can barely preserve distance for the unbounded input domain. In this work, we fill this gap by proposing a novel Lipschitz Regularized Transformer (LRFormer). Specifically, we present a new similarity function with the distance within Banach Space to ensure the Lipschitzness and also regularize the term by a contractive Lipschitz Bound. The proposed method is analyzed with a theoretical guarantee, providing a rigorous basis for its effectiveness and reliability. Extensive experiments conducted on standard vision benchmarks demonstrate that our method outperforms the state-of-the-art single forward pass approaches in prediction, calibration, and uncertainty estimation.

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Wenqian Ye, Yunsheng Ma, Xu Cao, Kun Tang. 2023-06-12. Mitigating Transformer Overconfidence via Lipschitz Regularization. https://arxiv.org/abs/2306.06849

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