Just rotate it! Uncertainty estimation in closed-source models via multiple queries

• URL: http://arxiv.org/abs/2405.13864v1
• Date: Wed, 22 May 2024 17:45:38 GMT
• Title: Just rotate it! Uncertainty estimation in closed-source models via multiple queries
• Authors: Konstantinos Pitas, Julyan Arbel,
• Abstract summary: We propose a simple and effective method to estimate the uncertainty of closed-source deep neural network image classification models. We demonstrate significant improvements in the calibration of uncertainty estimates compared to the naive baseline of assigning 100% confidence to all predictions.
• Score: 3.8121150313479655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a simple and effective method to estimate the uncertainty of closed-source deep neural network image classification models. Given a base image, our method creates multiple transformed versions and uses them to query the top-1 prediction of the closed-source model. We demonstrate significant improvements in the calibration of uncertainty estimates compared to the naive baseline of assigning 100\% confidence to all predictions. While we initially explore Gaussian perturbations, our empirical findings indicate that natural transformations, such as rotations and elastic deformations, yield even better-calibrated predictions. Furthermore, through empirical results and a straightforward theoretical analysis, we elucidate the reasons behind the superior performance of natural transformations over Gaussian noise. Leveraging these insights, we propose a transfer learning approach that further improves our calibration results.

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