STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains

• URL: http://arxiv.org/abs/2104.11596v1
• Date: Fri, 23 Apr 2021 13:46:26 GMT
• Title: STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains
• Authors: Fabian Gr\"oger, Anne-Marie Rickmann, Christian Wachinger
• Abstract summary: We propose an unsupervised domain adaptation (UDA) approach for white matter hyperintensity (WMH) segmentation. We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty. Our results on WMH segmentation across datasets demonstrate the significant improvement of STRUDEL with respect to standard self-training.
• Score: 4.812718493682454
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose an unsupervised domain adaptation (UDA) approach for white matter hyperintensity (WMH) segmentation, which uses Self-Training with Uncertainty DEpendent Label refinement (STRUDEL). Self-training has recently been introduced as a highly effective method for UDA, which is based on self-generated pseudo labels. However, pseudo labels can be very noisy and therefore deteriorate model performance. We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty. STRUDEL is further improved by incorporating the segmentation output of an existing method in the pseudo label generation that showed high robustness for WMH segmentation. In our experiments, we evaluate STRUDEL with a standard U-Net and a modified network with a higher receptive field. Our results on WMH segmentation across datasets demonstrate the significant improvement of STRUDEL with respect to standard self-training.

Related papers

• Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement [5.865750284677784]
  Adversarial training (AT) is one of the most effective ways to obtain the robustness of deep neural networks against adversarial attacks. AT methods suffer from robust overfitting, i.e., a significant generalization gap between the training and testing curves. We propose a label refinement approach for AT, which self-refines a more accurate and informative label distribution from over-confident hard labels.
  arXiv  Detail & Related papers  (2024-03-14T04:48:31Z)
• Unsupervised Domain Adaptation for Semantic Segmentation with Pseudo Label Self-Refinement [9.69089112870202]
  We propose an auxiliary pseudo-label refinement network (PRN) for online refining of the pseudo labels and also localizing the pixels whose predicted labels are likely to be noisy. We evaluate our approach on benchmark datasets with three different domain shifts, and our approach consistently performs significantly better than the previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-25T20:31:07Z)
• Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling [29.744621356187764]
  This paper presents SeqUST, a novel uncertain-aware self-training framework for Neural sequence labeling (NSL) We incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation at the token level and then select reliable language tokens from unlabeled data. A well-designed masked sequence labeling task with a noise-robust loss supports robust training, which aims to suppress the problem of noisy pseudo labels.
  arXiv  Detail & Related papers  (2023-02-17T02:40:04Z)
• Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation [18.807921765977415]
  We propose a hard-aware instance adaptive self-training framework for UDA on the task of semantic segmentation. We develop a novel pseudo-label generation strategy with an instance adaptive selector. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and Cityscapes to Oxford RobotCar demonstrate the superior performance of our approach.
  arXiv  Detail & Related papers  (2023-02-14T11:52:26Z)
• Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection [149.23913018423022]
  Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Two-stage self-training methods have achieved significant improvements by self-generating pseudo labels. We propose an enhancement framework by exploiting completeness and uncertainty properties for effective self-training.
  arXiv  Detail & Related papers  (2022-12-08T05:53:53Z)
• Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model [26.074500538428364]
  unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain. Recently, deep self-training presents a powerful means for UDA, involving an iterative process of predicting the target domain. We resort to the energy-based model and constrain the training of the unlabeled target sample with an energy function minimization objective.
  arXiv  Detail & Related papers  (2022-08-26T22:50:23Z)
• Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning [104.00026716576546]
  We propose to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations. We show that our proposed method outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-26T16:03:55Z)
• Cycle Self-Training for Domain Adaptation [85.14659717421533]
  Cycle Self-Training (CST) is a principled self-training algorithm that enforces pseudo-labels to generalize across domains. CST recovers target ground truth, while both invariant feature learning and vanilla self-training fail. Empirical results indicate that CST significantly improves over prior state-of-the-arts in standard UDA benchmarks.
  arXiv  Detail & Related papers  (2021-03-05T10:04:25Z)
• In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning [53.1047775185362]
  Pseudo-labeling (PL) is a general SSL approach that does not have this constraint but performs relatively poorly in its original formulation. We argue that PL underperforms due to the erroneous high confidence predictions from poorly calibrated models. We propose an uncertainty-aware pseudo-label selection (UPS) framework which improves pseudo labeling accuracy by drastically reducing the amount of noise encountered in the training process.
  arXiv  Detail & Related papers  (2021-01-15T23:29:57Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• Two-phase Pseudo Label Densification for Self-training based Domain Adaptation [93.03265290594278]
  We propose a novel Two-phase Pseudo Label Densification framework, referred to as TPLD. In the first phase, we use sliding window voting to propagate the confident predictions, utilizing intrinsic spatial-correlations in the images. In the second phase, we perform a confidence-based easy-hard classification. To ease the training process and avoid noisy predictions, we introduce the bootstrapping mechanism to the original self-training loss.
  arXiv  Detail & Related papers  (2020-12-09T02:35:25Z)

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.