CycleMix: Mixing Source Domains for Domain Generalization in Style-Dependent Data

• URL: http://arxiv.org/abs/2407.13421v2
• Date: Wed, 24 Jul 2024 13:09:22 GMT
• Title: CycleMix: Mixing Source Domains for Domain Generalization in Style-Dependent Data
• Authors: Aristotelis Ballas, Christos Diou,
• Abstract summary: In the case of image classification, one frequent reason that algorithms fail to generalize is that they rely on spurious correlations present in training data. These associations may not be present in the unseen test data, leading to significant degradation of their effectiveness. In this work, we attempt to mitigate this Domain Generalization problem by training a robust feature extractor which disregards features attributed to image-style but infers based on style-invariant image representations.
• Score: 5.124256074746721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As deep learning-based systems have become an integral part of everyday life, limitations in their generalization ability have begun to emerge. Machine learning algorithms typically rely on the i.i.d. assumption, meaning that their training and validation data are expected to follow the same distribution, which does not necessarily hold in practice. In the case of image classification, one frequent reason that algorithms fail to generalize is that they rely on spurious correlations present in training data, such as associating image styles with target classes. These associations may not be present in the unseen test data, leading to significant degradation of their effectiveness. In this work, we attempt to mitigate this Domain Generalization (DG) problem by training a robust feature extractor which disregards features attributed to image-style but infers based on style-invariant image representations. To achieve this, we train CycleGAN models to learn the different styles present in the training data and randomly mix them together to create samples with novel style attributes to improve generalization. Experimental results on the PACS DG benchmark validate the proposed method.

Related papers

• FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain Generalization [19.0284321951354]
  Domain Generalization techniques aim to enhance model robustness by simulating novel data distributions during training. We propose FDS, Feedback-guided Domain Synthesis, a novel strategy that employs diffusion models to synthesize novel, pseudo-domains. Our evaluations demonstrate that this methodology sets new benchmarks in domain generalization performance across a range of challenging datasets.
  arXiv  Detail & Related papers  (2024-07-04T02:45:29Z)
• DG-TTA: Out-of-domain medical image segmentation through Domain Generalization and Test-Time Adaptation [43.842694540544194]
  We propose to combine domain generalization and test-time adaptation to create a highly effective approach for reusing pre-trained models in unseen target domains. We demonstrate that our method, combined with pre-trained whole-body CT models, can effectively segment MR images with high accuracy.
  arXiv  Detail & Related papers  (2023-12-11T10:26:21Z)
• Activate and Reject: Towards Safe Domain Generalization under Category Shift [71.95548187205736]
  We study a practical problem of Domain Generalization under Category Shift (DGCS) It aims to simultaneously detect unknown-class samples and classify known-class samples in the target domains. Compared to prior DG works, we face two new challenges: 1) how to learn the concept of unknown'' during training with only source known-class samples, and 2) how to adapt the source-trained model to unseen environments.
  arXiv  Detail & Related papers  (2023-10-07T07:53:12Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• 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)
• Single Domain Generalization via Normalised Cross-correlation Based Convolutions [14.306250516592304]
  Single Domain Generalization aims to train robust models using data from a single source. We propose a novel operator called XCNorm that computes the normalized cross-correlation between weights and an input feature patch. We show that deep neural networks composed of this operator are robust to common semantic distribution shifts.
  arXiv  Detail & Related papers  (2023-07-12T04:15:36Z)
• CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification [6.9596321268519326]
  In pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task. We propose the CellMix framework, which employs a novel distribution-oriented in-place shuffle approach. Our experiments in pathology image classification tasks demonstrate state-of-the-art (SOTA) performance on 7 distinct datasets.
  arXiv  Detail & Related papers  (2023-01-27T03:17:35Z)
• Deep face recognition with clustering based domain adaptation [57.29464116557734]
  We propose a new clustering-based domain adaptation method designed for face recognition task in which the source and target domain do not share any classes. Our method effectively learns the discriminative target feature by aligning the feature domain globally, and, at the meantime, distinguishing the target clusters locally.
  arXiv  Detail & Related papers  (2022-05-27T12:29:11Z)
• General Greedy De-bias Learning [163.65789778416172]
  We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
  arXiv  Detail & Related papers  (2021-12-20T14:47:32Z)
• Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization [118.50301177912381]
  We show that Adam can converge to different solutions of the objective with provably different errors, even with weight decay globalization. We show that if convex, and the weight decay regularization is employed, any optimization algorithms including Adam will converge to the same solution.
  arXiv  Detail & Related papers  (2021-08-25T17:58:21Z)

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.