Label Alignment Regularization for Distribution Shift

• URL: http://arxiv.org/abs/2211.14960v5
• Date: Wed, 11 Sep 2024 15:34:51 GMT
• Title: Label Alignment Regularization for Distribution Shift
• Authors: Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart, Philip H. S. Torr, Yangchen Pan,
• Abstract summary: Recent work has highlighted the label alignment property (LAP) in supervised learning, where the vector of all labels in the dataset is mostly in the span of the top few singular vectors of the data matrix. We propose a regularization method for unsupervised domain adaptation that encourages alignment between the predictions in the target domain and its top singular vectors. We report improved performance over domain adaptation baselines in well-known tasks such as MNIST-USPS domain adaptation and cross-lingual sentiment analysis.
• Score: 63.228879525056904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work has highlighted the label alignment property (LAP) in supervised learning, where the vector of all labels in the dataset is mostly in the span of the top few singular vectors of the data matrix. Drawing inspiration from this observation, we propose a regularization method for unsupervised domain adaptation that encourages alignment between the predictions in the target domain and its top singular vectors. Unlike conventional domain adaptation approaches that focus on regularizing representations, we instead regularize the classifier to align with the unsupervised target data, guided by the LAP in both the source and target domains. Theoretical analysis demonstrates that, under certain assumptions, our solution resides within the span of the top right singular vectors of the target domain data and aligns with the optimal solution. By removing the reliance on the commonly used optimal joint risk assumption found in classic domain adaptation theory, we showcase the effectiveness of our method on addressing problems where traditional domain adaptation methods often fall short due to high joint error. Additionally, we report improved performance over domain adaptation baselines in well-known tasks such as MNIST-USPS domain adaptation and cross-lingual sentiment analysis.

Related papers

• Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation [108.40945109477886]
  We propose a novel SSDA approach named Graph-based Adaptive Betweenness Clustering (G-ABC) for achieving categorical domain alignment. Our method outperforms previous state-of-the-art SSDA approaches, demonstrating the superiority of the proposed G-ABC algorithm.
  arXiv  Detail & Related papers  (2024-01-21T09:57:56Z)
• Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction [62.41511766918932]
  Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios. We propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagrees on the unlabelled target data.
  arXiv  Detail & Related papers  (2023-02-28T16:31:17Z)
• Domain Generalization through the Lens of Angular Invariance [44.76809026901016]
  Domain generalization (DG) aims at generalizing a classifier trained on multiple source domains to an unseen target domain with domain shift. We propose a novel deep DG method called Angular Invariance Domain Generalization Network (AIDGN)
  arXiv  Detail & Related papers  (2022-10-28T02:05:38Z)
• Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation [14.050836886292869]
  We introduce feature-level space-shaping regularization strategies to reduce the domain discrepancy in semantic segmentation. We verify the effectiveness of such methods in the autonomous driving setting.
  arXiv  Detail & Related papers  (2021-04-06T16:07:22Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)
• Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation [70.45936509510528]
  Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain. It is essential to align target data with only a small set of source data.
  arXiv  Detail & Related papers  (2020-08-26T03:18:53Z)
• Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment [27.671964294233756]
  In this study, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain. The success of unsupervised domain adaptation largely relies on the cross-domain feature alignment. We introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of the prior distribution. In such an indirect way, the distributions over the samples from the two domains will be constructed on a common feature space, i.e., the space of the prior.
  arXiv  Detail & Related papers  (2020-06-23T05:33:54Z)
• Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning [8.316259570013813]
  We propose a novel domain adaptation approach, which can thoroughly explore the data distribution structure of target domain. We regard the samples within the same cluster in target domain as a whole rather than individuals and assigns pseudo-labels to the target cluster by class centroid matching. An efficient iterative optimization algorithm is designed to solve the objective function of our proposal with theoretical convergence guarantee.
  arXiv  Detail & Related papers  (2020-03-20T16:59:27Z)

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.