Predicting Out-of-Distribution Error with Confidence Optimal Transport

• URL: http://arxiv.org/abs/2302.05018v1
• Date: Fri, 10 Feb 2023 02:27:13 GMT
• Title: Predicting Out-of-Distribution Error with Confidence Optimal Transport
• Authors: Yuzhe Lu, Zhenlin Wang, Runtian Zhai, Soheil Kolouri, Joseph Campbell, Katia Sycara
• Abstract summary: We present a simple yet effective method to predict a model's performance on an unknown distribution without any addition annotation. We show that our method, Confidence Optimal Transport (COT), provides robust estimates of a model's performance on a target domain. Despite its simplicity, our method achieves state-of-the-art results on three benchmark datasets and outperforms existing methods by a large margin.
• Score: 17.564313038169434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops. Being able to estimate a model's performance on test data is important in practice as it indicates when to trust to model's decisions. We present a simple yet effective method to predict a model's performance on an unknown distribution without any addition annotation. Our approach is rooted in the Optimal Transport theory, viewing test samples' output softmax scores from deep neural networks as empirical samples from an unknown distribution. We show that our method, Confidence Optimal Transport (COT), provides robust estimates of a model's performance on a target domain. Despite its simplicity, our method achieves state-of-the-art results on three benchmark datasets and outperforms existing methods by a large margin.

Related papers

• Source-Free Domain-Invariant Performance Prediction [68.39031800809553]
  We propose a source-free approach centred on uncertainty-based estimation, using a generative model for calibration in the absence of source data. Our experiments on benchmark object recognition datasets reveal that existing source-based methods fall short with limited source sample availability. Our approach significantly outperforms the current state-of-the-art source-free and source-based methods, affirming its effectiveness in domain-invariant performance estimation.
  arXiv  Detail & Related papers  (2024-08-05T03:18:58Z)
• Refining 3D Point Cloud Normal Estimation via Sample Selection [13.207964615561261]
  We introduce a fundamental framework for normal estimation, enhancing existing model through the incorporation of global information and various constraint mechanisms. We also utilize existing orientation methods to correct estimated non-oriented normals, achieving state-of-the-art performance in both oriented and non-oriented tasks.
  arXiv  Detail & Related papers  (2024-05-20T02:06:10Z)
• Self-Supervised Dataset Distillation for Transfer Learning [77.4714995131992]
  We propose a novel problem of distilling an unlabeled dataset into a set of small synthetic samples for efficient self-supervised learning (SSL) We first prove that a gradient of synthetic samples with respect to a SSL objective in naive bilevel optimization is textitbiased due to randomness originating from data augmentations or masking. We empirically validate the effectiveness of our method on various applications involving transfer learning.
  arXiv  Detail & Related papers  (2023-10-10T10:48:52Z)
• Characterizing Out-of-Distribution Error via Optimal Transport [15.284665509194134]
  Methods of predicting a model's performance on OOD data without labels are important for machine learning safety. We introduce a novel method for estimating model performance by leveraging optimal transport theory. We show that our approaches significantly outperform existing state-of-the-art methods with an up to 3x lower prediction error.
  arXiv  Detail & Related papers  (2023-05-25T01:37:13Z)
• Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities [17.082695183953486]
  A popular workaround is to train a robust model by re-weighting training examples based on a secondary biased model. Here, the underlying assumption is that the biased model resorts to shortcut features. We introduce a fine-tuning strategy that incorporates the similarity between the main and biased model attribution scores in a Product of Experts loss function.
  arXiv  Detail & Related papers  (2023-02-06T15:21:41Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Sampling from Arbitrary Functions via PSD Models [55.41644538483948]
  We take a two-step approach by first modeling the probability distribution and then sampling from that model. We show that these models can approximate a large class of densities concisely using few evaluations, and present a simple algorithm to effectively sample from these models.
  arXiv  Detail & Related papers  (2021-10-20T12:25:22Z)
• Model-based Policy Optimization with Unsupervised Model Adaptation [37.09948645461043]
  We investigate how to bridge the gap between real and simulated data due to inaccurate model estimation for better policy optimization. We propose a novel model-based reinforcement learning framework AMPO, which introduces unsupervised model adaptation. Our approach achieves state-of-the-art performance in terms of sample efficiency on a range of continuous control benchmark tasks.
  arXiv  Detail & Related papers  (2020-10-19T14:19:42Z)
• Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance [70.31427277842239]
  We introduce a novel debiasing method called confidence regularization. It discourages models from exploiting biases while enabling them to receive enough incentive to learn from all the training examples. We evaluate our method on three NLU tasks and show that, in contrast to its predecessors, it improves the performance on out-of-distribution datasets.
  arXiv  Detail & Related papers  (2020-05-01T11:22:55Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.