Feature-Space Planes Searcher: A Universal Domain Adaptation Framework for Interpretability and Computational Efficiency

• URL: http://arxiv.org/abs/2508.18693v1
• Date: Tue, 26 Aug 2025 05:39:21 GMT
• Title: Feature-Space Planes Searcher: A Universal Domain Adaptation Framework for Interpretability and Computational Efficiency
• Authors: Zhitong Cheng, Yiran Jiang, Yulong Ge, Yufeng Li, Zhongheng Qin, Rongzhi Lin, Jianwei Ma,
• Abstract summary: Current unsupervised domain adaptation methods rely on fine-tuning feature extractors.<n>We propose Feature-space Planes Searcher (FPS) as a novel domain adaptation framework.<n>We show that FPS achieves competitive or superior performance to state-of-the-art methods.
• Score: 7.889121135601528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain shift, characterized by degraded model performance during transition from labeled source domains to unlabeled target domains, poses a persistent challenge for deploying deep learning systems. Current unsupervised domain adaptation (UDA) methods predominantly rely on fine-tuning feature extractors - an approach limited by inefficiency, reduced interpretability, and poor scalability to modern architectures. Our analysis reveals that models pretrained on large-scale data exhibit domain-invariant geometric patterns in their feature space, characterized by intra-class clustering and inter-class separation, thereby preserving transferable discriminative structures. These findings indicate that domain shifts primarily manifest as boundary misalignment rather than feature degradation. Unlike fine-tuning entire pre-trained models - which risks introducing unpredictable feature distortions - we propose the Feature-space Planes Searcher (FPS): a novel domain adaptation framework that optimizes decision boundaries by leveraging these geometric patterns while keeping the feature encoder frozen. This streamlined approach enables interpretative analysis of adaptation while substantially reducing memory and computational costs through offline feature extraction, permitting full-dataset optimization in a single computation cycle. Evaluations on public benchmarks demonstrate that FPS achieves competitive or superior performance to state-of-the-art methods. FPS scales efficiently with multimodal large models and shows versatility across diverse domains including protein structure prediction, remote sensing classification, and earthquake detection. We anticipate FPS will provide a simple, effective, and generalizable paradigm for transfer learning, particularly in domain adaptation tasks. .

Related papers

• Simulating Distribution Dynamics: Liquid Temporal Feature Evolution for Single-Domain Generalized Object Detection [58.25418970608328]
  Single-Domain Generalized Object Detection (Single-DGOD) aims to transfer a detector trained on one source domain to multiple unknown domains.<n>Existing methods for Single-DGOD typically rely on discrete data augmentation or static perturbation methods to expand data diversity.<n>We propose a new method, which simulates the progressive evolution of features from the source domain to simulated latent distributions.
  arXiv  Detail & Related papers  (2025-11-13T03:10:39Z)
• Domain Adaptation via Feature Refinement [0.3867363075280543]
  We propose Domain Adaptation via Feature Refinement (DAFR2), a simple yet effective framework for unsupervised domain adaptation under distribution shift.<n>The proposed method combines three key components: adaptation of Batch Normalization statistics using unlabeled target data, feature distillation from a source-trained model and hypothesis transfer.
  arXiv  Detail & Related papers  (2025-08-22T06:32:19Z)
• DIDS: Domain Impact-aware Data Sampling for Large Language Model Training [61.10643823069603]
  We present Domain Impact-aware Data Sampling (DIDS) for large language models.<n>DIDS group training data based on learning effects, where a proxy language model and dimensionality reduction are employed.<n>It achieves 3.4% higher average performance while maintaining comparable training efficiency.
  arXiv  Detail & Related papers  (2025-04-17T13:09:38Z)
• Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization [68.41367635546183]
  Single Domain Generalization aims to train models with consistent performance across diverse scenarios using data from a single source.<n>We propose Discriminative Domain Reassembly and Soft-Fusion (DRSF), a training framework leveraging synthetic data to improve model generalization.
  arXiv  Detail & Related papers  (2025-03-17T18:08:03Z)
• From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation [1.93061220186624]
  We propose an innovative framework that enhances data representation quality through probabilistic modeling and contrastive learning.<n>Specifically, we combine deterministic features with uncertainty modeling to capture comprehensive feature distributions.<n>We show that the proposed framework significantly improves segmentation performance, providing a robust solution to domain generalization challenges in medical image segmentation.
  arXiv  Detail & Related papers  (2024-12-07T07:41:04Z)
• Gradient Alignment for Cross-Domain Face Anti-Spoofing [26.517887637150594]
  We introduce GAC-FAS, a novel learning objective that encourages the model to converge towards an optimal flat minimum. Unlike conventional sharpness-aware minimizers, GAC-FAS identifies ascending points for each domain and regulates the generalization gradient updates. We demonstrate the efficacy of GAC-FAS through rigorous testing on challenging cross-domain FAS datasets.
  arXiv  Detail & Related papers  (2024-02-29T02:57:44Z)
• Robust Unsupervised Domain Adaptation by Retaining Confident Entropy via Edge Concatenation [7.953644697658355]
  Unsupervised domain adaptation can mitigate the need for extensive pixel-level annotations to train semantic segmentation networks. We introduce a novel approach to domain adaptation, leveraging the synergy of internal and external information within entropy-based adversarial networks. We devised a probability-sharing network that integrates diverse information for more effective segmentation.
  arXiv  Detail & Related papers  (2023-10-11T02:50:16Z)
• A Novel Cross-Perturbation for Single Domain Generalization [54.612933105967606]
  Single domain generalization aims to enhance the ability of the model to generalize to unknown domains when trained on a single source domain. The limited diversity in the training data hampers the learning of domain-invariant features, resulting in compromised generalization performance. We propose CPerb, a simple yet effective cross-perturbation method to enhance the diversity of the training data.
  arXiv  Detail & Related papers  (2023-08-02T03:16:12Z)
• Randomized Adversarial Style Perturbations for Domain Generalization [49.888364462991234]
  We propose a novel domain generalization technique, referred to as Randomized Adversarial Style Perturbation (RASP) The proposed algorithm perturbs the style of a feature in an adversarial direction towards a randomly selected class, and makes the model learn against being misled by the unexpected styles observed in unseen target domains. We evaluate the proposed algorithm via extensive experiments on various benchmarks and show that our approach improves domain generalization performance, especially in large-scale benchmarks.
  arXiv  Detail & Related papers  (2023-04-04T17:07:06Z)
• On Fine-Tuned Deep Features for Unsupervised Domain Adaptation [23.18781318003242]
  We study the potential of combining fine-tuned features and feature transformation based UDA methods for improved domain adaptation performance. Specifically, we integrate the prevalent progressive pseudo-labelling techniques into the fine-tuning framework to extract fine-tuned features. Experiments with multiple deep models including ResNet-50/101 and DeiT-small/base are conducted to demonstrate the combination of fine-tuned features.
  arXiv  Detail & Related papers  (2022-10-25T15:07:04Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)

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.