A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation

• URL: http://arxiv.org/abs/2507.22632v1
• Date: Wed, 30 Jul 2025 12:53:08 GMT
• Title: A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation
• Authors: Elif Vural, Huseyin Karaca,
• Abstract summary: Domain adaptation seeks to leverage the abundant label information in a source domain to improve classification performance in a target domain with limited labels.<n>Most existing theoretical analyses focus on simplified settings where the source and target domains share the same input space.<n>We present a comprehensive theoretical study of domain adaptation algorithms based on domain alignment.
• Score: 1.9567015559455132
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Domain adaptation seeks to leverage the abundant label information in a source domain to improve classification performance in a target domain with limited labels. While the field has seen extensive methodological development, its theoretical foundations remain relatively underexplored. Most existing theoretical analyses focus on simplified settings where the source and target domains share the same input space and relate target-domain performance to measures of domain discrepancy. Although insightful, these analyses may not fully capture the behavior of modern approaches that align domains into a shared space via feature transformations. In this paper, we present a comprehensive theoretical study of domain adaptation algorithms based on domain alignment. We consider the joint learning of domain-aligning feature transformations and a shared classifier in a semi-supervised setting. We first derive generalization bounds in a broad setting, in terms of covering numbers of the relevant function classes. We then extend our analysis to characterize the sample complexity of domain-adaptive neural networks employing maximum mean discrepancy (MMD) or adversarial objectives. Our results rely on a rigorous analysis of the covering numbers of these architectures. We show that, for both MMD-based and adversarial models, the sample complexity admits an upper bound that scales quadratically with network depth and width. Furthermore, our analysis suggests that in semi-supervised settings, robustness to limited labeled target data can be achieved by scaling the target loss proportionally to the square root of the number of labeled target samples. Experimental evaluation in both shallow and deep settings lends support to our theoretical findings.

Related papers

• What's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization [10.079844840768054]
  Domain Generalization aims to develop models that can generalize to novel and unseen data distributions.<n>We study how model architectures and pre-training objectives impact feature richness.<n>Our framework improves generalization to unseen domains by a maximum test accuracy improvement of over 4%.
  arXiv  Detail & Related papers  (2025-03-09T17:29:01Z)
• Cross Contrasting Feature Perturbation for Domain Generalization [11.863319505696184]
  Domain generalization aims to learn a robust model from source domains that generalize well on unseen target domains. Recent studies focus on generating novel domain samples or features to diversify distributions complementary to source domains. We propose an online one-stage Cross Contrasting Feature Perturbation framework to simulate domain shift.
  arXiv  Detail & Related papers  (2023-07-24T03:27:41Z)
• Domain-aware Triplet loss in Domain Generalization [0.0]
  Domain shift is caused by discrepancies in the distributions of the testing and training data. We design a domainaware triplet loss for domain generalization to help the model to cluster similar semantic features. Our algorithm is designed to disperse domain information in the embedding space.
  arXiv  Detail & Related papers  (2023-03-01T14:02:01Z)
• Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge [22.285156929279207]
  Domain generalization aims at learning a universal model that performs well on unseen target domains. We propose a novel domain generalization framework called Wasserstein Distributionally Robust Domain Generalization (WDRDG)
  arXiv  Detail & Related papers  (2022-07-11T14:46:50Z)
• Compound Domain Generalization via Meta-Knowledge Encoding [55.22920476224671]
  We introduce Style-induced Domain-specific Normalization (SDNorm) to re-normalize the multi-modal underlying distributions. We harness the prototype representations, the centroids of classes, to perform relational modeling in the embedding space. Experiments on four standard Domain Generalization benchmarks reveal that COMEN exceeds the state-of-the-art performance without the need of domain supervision.
  arXiv  Detail & Related papers  (2022-03-24T11:54:59Z)
• Domain Generalisation for Object Detection under Covariate and Concept Shift [10.32461766065764]
  Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain-specific features. An approach to domain generalisation for object detection is proposed, the first such approach applicable to any object detection architecture.
  arXiv  Detail & Related papers  (2022-03-10T11:14:18Z)
• META: Mimicking Embedding via oThers' Aggregation for Generalizable Person Re-identification [68.39849081353704]
  Domain generalizable (DG) person re-identification (ReID) aims to test across unseen domains without access to the target domain data at training time. This paper presents a new approach called Mimicking Embedding via oThers' Aggregation (META) for DG ReID.
  arXiv  Detail & Related papers  (2021-12-16T08:06:50Z)
• Model-Based Domain Generalization [96.84818110323518]
  We propose a novel approach for the domain generalization problem called Model-Based Domain Generalization. Our algorithms beat the current state-of-the-art methods on the very-recently-proposed WILDS benchmark by up to 20 percentage points.
  arXiv  Detail & Related papers  (2021-02-23T00:59:02Z)
• Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation [74.3349233035632]
  Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) do not consider an inter-class variation within the target domain itself or estimated category. We introduce a learnable clustering module, and a novel domain adaptation framework called cross-domain grouping and alignment. Our method consistently boosts the adaptation performance in semantic segmentation, outperforming the state-of-the-arts on various domain adaptation settings.
  arXiv  Detail & Related papers  (2020-12-15T11:36:21Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)
• Bi-Directional Generation for Unsupervised Domain Adaptation [61.73001005378002]
  Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. Conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. We propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains.
  arXiv  Detail & Related papers  (2020-02-12T09:45:39Z)

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.