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.

Related papers

• Debias the Training of Diffusion Models [53.49637348771626]
  We provide theoretical evidence that the prevailing practice of using a constant loss weight strategy in diffusion models leads to biased estimation during the training phase. We propose an elegant and effective weighting strategy grounded in the theoretically unbiased principle. These analyses are expected to advance our understanding and demystify the inner workings of diffusion models.
  arXiv  Detail & Related papers  (2023-10-12T16:04:41Z)
• Multiclass Alignment of Confidence and Certainty for Network Calibration [10.15706847741555]
  Recent studies reveal that deep neural networks (DNNs) are prone to making overconfident predictions. We propose a new train-time calibration method, which features a simple, plug-and-play auxiliary loss known as multi-class alignment of predictive mean confidence and predictive certainty (MACC) Our method achieves state-of-the-art calibration performance for both in-domain and out-domain predictions.
  arXiv  Detail & Related papers  (2023-09-06T00:56:24Z)
• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• Sharp Calibrated Gaussian Processes [58.94710279601622]
  State-of-the-art approaches for designing calibrated models rely on inflating the Gaussian process posterior variance. We present a calibration approach that generates predictive quantiles using a computation inspired by the vanilla Gaussian process posterior variance. Our approach is shown to yield a calibrated model under reasonable assumptions.
  arXiv  Detail & Related papers  (2023-02-23T12:17:36Z)
• Quantifying Model Predictive Uncertainty with Perturbation Theory [21.591460685054546]
  We propose a framework for predictive uncertainty quantification of a neural network. We use perturbation theory from quantum physics to formulate a moment decomposition problem. Our approach provides fast model predictive uncertainty estimates with much greater precision and calibration.
  arXiv  Detail & Related papers  (2021-09-22T17:55:09Z)
• Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning [14.03923026690186]
  We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction. In most cases, the proposed model shows better performance compared to common architectures. Our model can also be used to detect challenging and controversial test samples.
  arXiv  Detail & Related papers  (2021-06-01T17:09:47Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Multiplicative noise and heavy tails in stochastic optimization [62.993432503309485]
  empirical optimization is central to modern machine learning, but its role in its success is still unclear. We show that it commonly arises in parameters of discrete multiplicative noise due to variance. A detailed analysis is conducted in which we describe on key factors, including recent step size, and data, all exhibit similar results on state-of-the-art neural network models.
  arXiv  Detail & Related papers  (2020-06-11T09:58:01Z)
• Bayesian Deep Learning and a Probabilistic Perspective of Generalization [56.69671152009899]
  We show that deep ensembles provide an effective mechanism for approximate Bayesian marginalization. We also propose a related approach that further improves the predictive distribution by marginalizing within basins of attraction.
  arXiv  Detail & Related papers  (2020-02-20T15:13:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.