GAMA++: Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer

• URL: http://arxiv.org/abs/2505.15241v1
• Date: Wed, 21 May 2025 08:16:35 GMT
• Title: GAMA++: Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer
• Authors: Kim Yun, Hana Satou, F Monkey,
• Abstract summary: GAMA++ is a novel framework that introduces (i) latent space disentanglement to isolate label-consistent manifold directions from nuisance factors, and (ii) an adaptive contrastive perturbation strategy that tailors both on- and off-manifold exploration to class-specific manifold curvature and alignment discrepancy.<n>Our method achieves state-of-the-art results on DomainNet, Office-Home, and VisDA benchmarks under both standard and few-shot settings, with notable improvements in class-level alignment fidelity and boundary robustness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite progress in geometry-aware domain adaptation, current methods such as GAMA still suffer from two unresolved issues: (1) insufficient disentanglement of task-relevant and task-irrelevant manifold dimensions, and (2) rigid perturbation schemes that ignore per-class alignment asymmetries. To address this, we propose GAMA++, a novel framework that introduces (i) latent space disentanglement to isolate label-consistent manifold directions from nuisance factors, and (ii) an adaptive contrastive perturbation strategy that tailors both on- and off-manifold exploration to class-specific manifold curvature and alignment discrepancy. We further propose a cross-domain contrastive consistency loss that encourages local semantic clusters to align while preserving intra-domain diversity. Our method achieves state-of-the-art results on DomainNet, Office-Home, and VisDA benchmarks under both standard and few-shot settings, with notable improvements in class-level alignment fidelity and boundary robustness. GAMA++ sets a new standard for semantic geometry alignment in transfer learning.

Related papers

• Heterogeneous-Modal Unsupervised Domain Adaptation via Latent Space Bridging [12.171477896623148]
  We propose a novel setting called Heterogeneous-Modal Unsupervised Domain Adaptation (HMUDA)<n>HMUDA enables knowledge transfer between completely different modalities by leveraging a bridge domain containing unlabeled samples from both modalities.<n>We propose Latent Space Bridging (LSB), a specialized framework designed for the semantic segmentation task.
  arXiv  Detail & Related papers  (2025-06-19T02:31:51Z)
• GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation [0.0]
  GAMA is a structured framework that achieves explicit manifold alignment via adversarial perturbation guided by geometric information.<n>GAMA tightens the generalization bound via structured regularization and explicit alignment.<n> Empirical results on DomainNet, VisDA, and Office-Home demonstrate that GAMA consistently outperforms existing adversarial and adaptation methods.
  arXiv  Detail & Related papers  (2025-05-21T07:16:42Z)
• Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation [0.0]
  MAADA is a novel framework that decomposes adversarial perturbations into on-manifold and off-manifold components.<n>We show that MAADA consistently outperforms existing adversarial and adaptation methods in both unsupervised and few-shot settings.
  arXiv  Detail & Related papers  (2025-05-21T07:13:09Z)
• Open Set Domain Adaptation with Vision-language models via Gradient-aware Separation [0.6118897979046375]
  Open-Set Domain Adaptation (OSDA) confronts the dual challenge of aligning known-class distributions across domains.<n>We propose to harness Contrastive Language-Image Pretraining (CLIP) to address these limitations.
  arXiv  Detail & Related papers  (2025-05-16T12:31:17Z)
• Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation [62.68759523116924]
  Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting. We propose a novel feature space Amplitude Spectrum Transformation (AST)
  arXiv  Detail & Related papers  (2022-02-09T05:40:34Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning [74.76431541169342]
  Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. We propose a novel hierarchical semantic-visual adaptation (HSVA) framework to align semantic and visual domains. Experiments on four benchmark datasets demonstrate HSVA achieves superior performance on both conventional and generalized ZSL.
  arXiv  Detail & Related papers  (2021-09-30T14:27:50Z)
• More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation [41.81843755299211]
  We propose a new UDA semantic segmentation approach based on domain assumption closeness to alleviate the above problems. Specifically, a prototype clustering strategy is applied to cluster pixels with the same semantic, which will better maintain associations among target domain pixels. Experiments conducted on GTA5 and SYNTHIA proved the effectiveness of our method.
  arXiv  Detail & Related papers  (2021-05-07T10:24:18Z)
• 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)
• 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)
• Contradictory Structure Learning for Semi-supervised Domain Adaptation [67.89665267469053]
  Current adversarial adaptation methods attempt to align the cross-domain features. Two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain. We propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures.
  arXiv  Detail & Related papers  (2020-02-06T22:58:20Z)

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.