D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Amplitude and Pixel Spaces

• URL: http://arxiv.org/abs/2511.11286v1
• Date: Fri, 14 Nov 2025 13:22:10 GMT
• Title: D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Amplitude and Pixel Spaces
• Authors: Ruoqi Wang, Haitao Wang, Shaojie Guo, Qiong Luo,
• Abstract summary: We propose D-GAP (Dataset-agnostic and Gradient-guided augmentation in Amplitude and Pixel spaces) to improve robustness in real-world computer vision applications.<n>D-GAP computes sensitivity maps in the frequency space from task gradients, which reflect how strongly the model responds to different frequency components.<n>Experiments on four real-world datasets and three domain-adaptation benchmarks show that D-GAP consistently outperforms both generic and dataset-specific augmentations.
• Score: 5.0464991600543305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision applications, where shifts in image background, style, and acquisition instruments always degrade model performance. Generic augmentations show inconsistent gains under such shifts, whereas dataset-specific augmentations require expert knowledge and prior analysis. Moreover, prior studies show that neural networks adapt poorly to domain shifts because they exhibit a learning bias to domain-specific frequency components. Perturbing frequency values can mitigate such bias but overlooks pixel-level details, leading to suboptimal performance. To address these problems, we propose D-GAP (Dataset-agnostic and Gradient-guided augmentation in Amplitude and Pixel spaces), improving OOD robustness by introducing targeted augmentation in both the amplitude space (frequency space) and pixel space. Unlike conventional handcrafted augmentations, D-GAP computes sensitivity maps in the frequency space from task gradients, which reflect how strongly the model responds to different frequency components, and uses the maps to adaptively interpolate amplitudes between source and target samples. This way, D-GAP reduces the learning bias in frequency space, while a complementary pixel-space blending procedure restores fine spatial details. Extensive experiments on four real-world datasets and three domain-adaptation benchmarks show that D-GAP consistently outperforms both generic and dataset-specific augmentations, improving average OOD performance by +5.3% on real-world datasets and +1.8% on benchmark datasets.

Related papers

• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• AugmentGest: Can Random Data Cropping Augmentation Boost Gesture Recognition Performance? [49.64902130083662]
  This paper proposes a comprehensive data augmentation framework that integrates geometric transformations, random variations, rotation, zooming and intensity-based transformations.<n>The proposed augmentation strategy is evaluated on three models: multi-stream e2eET, FPPR point cloud-based hand gesture recognition (HGR), and DD-Network.
  arXiv  Detail & Related papers  (2025-06-08T16:43:05Z)
• Exploring Generalized Gait Recognition: Reducing Redundancy and Noise within Indoor and Outdoor Datasets [24.242460774158463]
  Generalized gait recognition aims to achieve robust performance across diverse domains.<n>Mixed-dataset training is widely used to enhance generalization.<n>We propose a unified framework that systematically improves cross-domain gait recognition.
  arXiv  Detail & Related papers  (2025-05-21T06:46:09Z)
• Improving Out-of-Domain Robustness with Targeted Augmentation in Frequency and Pixel Spaces [3.0931146230148174]
  In real-world scenarios, models trained with generic augmentations can only improve marginally when generalized under distribution shifts toward unlabeled target domains.<n>We propose Frequency-Pixel Connect, a domain-adaptation framework that enhances OOD robustness by introducing a targeted augmentation in both the frequency space and pixel space.<n>We demonstrate that Frequency-Pixel Connect significantly improves cross-domain connectivity and outperforms previous generic methods on four diverse real-world benchmarks across vision, medical, audio, and astronomical domains.
  arXiv  Detail & Related papers  (2025-05-18T09:15:40Z)
• Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data [3.1951121258423334]
  We develop an instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes. We also develop an instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes.
  arXiv  Detail & Related papers  (2024-11-07T13:53:13Z)
• 3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction [50.07071392673984]
  Existing methods learn 3D rotations parametrized in the spatial domain using angles or quaternions. We propose a frequency-domain approach that directly predicts Wigner-D coefficients for 3D rotation regression. Our method achieves state-of-the-art results on benchmarks such as ModelNet10-SO(3) and PASCAL3D+.
  arXiv  Detail & Related papers  (2024-11-01T12:50:38Z)
• How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit? [15.550663626482903]
  We investigate the efficacy of data augmentations to close the domain gap in spaceborne computer vision. We propose two novel data augmentations specifically developed to emulate the visual effects observed in orbital imagery.
  arXiv  Detail & Related papers  (2024-10-21T08:24:46Z)
• CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
  We propose a novel approach to improve NeRF's performance with sparse inputs. We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
  arXiv  Detail & Related papers  (2024-03-25T15:56:17Z)
• Data Augmentation for Traffic Classification [54.92823760790628]
  Data Augmentation (DA) is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks. DA has struggled to gain traction in networking contexts, particularly in Traffic Classification (TC) tasks.
  arXiv  Detail & Related papers  (2024-01-19T15:25:09Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)

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.