Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement

• URL: http://arxiv.org/abs/2305.01481v1
• Date: Tue, 2 May 2023 15:02:17 GMT
• Title: Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement
• Authors: Ailin Deng, Miao Xiong, Bryan Hooi
• Abstract summary: We estimate a model's reliability by measuring emphthe agreement between its latent space, and the latent space of a foundation model. To overcome this incoherence issue, we design a emphneighborhood agreement measure between latent spaces. We show that fusing neighborhood agreement into a model's predictive confidence in a post-hoc way significantly improves its reliability.
• Score: 27.067551390457567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable application of machine learning is of primary importance to the practical deployment of deep learning methods. A fundamental challenge is that models are often unreliable due to overconfidence. In this paper, we estimate a model's reliability by measuring \emph{the agreement between its latent space, and the latent space of a foundation model}. However, it is challenging to measure the agreement between two different latent spaces due to their incoherence, \eg, arbitrary rotations and different dimensionality. To overcome this incoherence issue, we design a \emph{neighborhood agreement measure} between latent spaces and find that this agreement is surprisingly well-correlated with the reliability of a model's predictions. Further, we show that fusing neighborhood agreement into a model's predictive confidence in a post-hoc way significantly improves its reliability. Theoretical analysis and extensive experiments on failure detection across various datasets verify the effectiveness of our method on both in-distribution and out-of-distribution settings.

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