Optimal Transport for Domain Adaptation through Gaussian Mixture Models

• URL: http://arxiv.org/abs/2403.13847v2
• Date: Wed, 22 Jan 2025 12:47:49 GMT
• Title: Optimal Transport for Domain Adaptation through Gaussian Mixture Models
• Authors: Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, Antoine Souloumiac,
• Abstract summary: Machine learning systems operate under the assumption that training and test data are sampled from a fixed probability distribution. In this work, we explore optimal transport between Gaussian Mixture Models (GMMs), which is conveniently written in terms of the components of source and target GMMs. We experiment with 9 benchmarks, with a total of $85$ adaptation tasks, showing that our methods are more efficient than previous shallow domain adaptation methods.
• Score: 7.292229955481438
• License:
• Abstract: Machine learning systems operate under the assumption that training and test data are sampled from a fixed probability distribution. However, this assumptions is rarely verified in practice, as the conditions upon which data was acquired are likely to change. In this context, the adaptation of the unsupervised domain requires minimal access to the data of the new conditions for learning models robust to changes in the data distribution. Optimal transport is a theoretically grounded tool for analyzing changes in distribution, especially as it allows the mapping between domains. However, these methods are usually computationally expensive as their complexity scales cubically with the number of samples. In this work, we explore optimal transport between Gaussian Mixture Models (GMMs), which is conveniently written in terms of the components of source and target GMMs. We experiment with 9 benchmarks, with a total of $85$ adaptation tasks, showing that our methods are more efficient than previous shallow domain adaptation methods, and they scale well with number of samples $n$ and dimensions $d$.

Related papers

• Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation [84.82153655786183]
  We propose a novel framework called Informative Data Mining (IDM) to enable efficient one-shot domain adaptation for semantic segmentation. IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7%/55.4% on the GTA5/SYNTHIA to Cityscapes adaptation tasks.
  arXiv  Detail & Related papers  (2023-09-25T15:56:01Z)
• Train/Test-Time Adaptation with Retrieval [129.8579208970529]
  We introduce Train/Test-Time Adaptation with Retrieval ($rm T3AR$), a method to adapt models both at train and test time. $rm T3AR$ adapts a given model to the downstream task using refined pseudo-labels and a self-supervised contrastive objective function. Thanks to the retrieval module, our method gives the user or service provider the possibility to improve model adaptation on the downstream task.
  arXiv  Detail & Related papers  (2023-03-25T02:44:57Z)
• Robustness, Evaluation and Adaptation of Machine Learning Models in the Wild [4.304803366354879]
  We study causes of impaired robustness to domain shifts and present algorithms for training domain robust models. A key source of model brittleness is due to domain overfitting, which our new training algorithms suppress and instead encourage domain-general hypotheses.
  arXiv  Detail & Related papers  (2023-03-05T21:41:16Z)
• Few-Shot Adaptation of Pre-Trained Networks for Domain Shift [17.123505029637055]
  Deep networks are prone to performance degradation when there is a domain shift between the source (training) data and target (test) data. Recent test-time adaptation methods update batch normalization layers of pre-trained source models deployed in new target environments with streaming data to mitigate such performance degradation. We propose a framework for few-shot domain adaptation to address the practical challenges of data-efficient adaptation.
  arXiv  Detail & Related papers  (2022-05-30T16:49:59Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• 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)
• Simpler Certified Radius Maximization by Propagating Covariances [39.851641822878996]
  We show an algorithm for maximizing the certified radius on datasets including Cifar-10, ImageNet, and Places365. We show how satisfying these criteria yields an algorithm for maximizing the certified radius on datasets with moderate depth, with a small compromise in overall accuracy.
  arXiv  Detail & Related papers  (2021-04-13T01:38:36Z)
• Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation [120.69747175899421]
  Optimal Transport (OT) distances such as Wasserstein have been used in several areas such as GANs and domain adaptation. We propose a computationally-efficient dual form of the robust OT optimization that is amenable to modern deep learning applications. Our approach can train state-of-the-art GAN models on noisy datasets corrupted with outlier distributions.
  arXiv  Detail & Related papers  (2020-10-12T17:13:40Z)
• Adaptive Risk Minimization: Learning to Adapt to Domain Shift [109.87561509436016]
  A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution. In this work, we consider the problem setting of domain generalization, where the training data are structured into domains and there may be multiple test time shifts. We introduce the framework of adaptive risk minimization (ARM), in which models are directly optimized for effective adaptation to shift by learning to adapt on the training domains.
  arXiv  Detail & Related papers  (2020-07-06T17:59:30Z)
• Domain Adaptive Bootstrap Aggregating [5.444459446244819]
  bootstrap aggregating, or bagging, is a popular method for improving stability of predictive algorithms. This article proposes a domain adaptive bagging method coupled with a new iterative nearest neighbor sampler.
  arXiv  Detail & Related papers  (2020-01-12T20:02:58Z)

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.