Conditional Support Alignment for Domain Adaptation with Label Shift
- URL: http://arxiv.org/abs/2305.18458v1
- Date: Mon, 29 May 2023 05:20:18 GMT
- Title: Conditional Support Alignment for Domain Adaptation with Label Shift
- Authors: Anh T Nguyen, Lam Tran, Anh Tong, Tuan-Duy H. Nguyen, Toan Tran
- Abstract summary: Unlabelled domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on labeled samples on the source domain and unsupervised ones in the target domain.
We propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions.
- Score: 8.819673391477034
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Unsupervised domain adaptation (UDA) refers to a domain adaptation framework
in which a learning model is trained based on the labeled samples on the source
domain and unlabelled ones in the target domain. The dominant existing methods
in the field that rely on the classical covariate shift assumption to learn
domain-invariant feature representation have yielded suboptimal performance
under the label distribution shift between source and target domains. In this
paper, we propose a novel conditional adversarial support alignment (CASA)
whose aim is to minimize the conditional symmetric support divergence between
the source's and target domain's feature representation distributions, aiming
at a more helpful representation for the classification task. We also introduce
a novel theoretical target risk bound, which justifies the merits of aligning
the supports of conditional feature distributions compared to the existing
marginal support alignment approach in the UDA settings. We then provide a
complete training process for learning in which the objective optimization
functions are precisely based on the proposed target risk bound. Our empirical
results demonstrate that CASA outperforms other state-of-the-art methods on
different UDA benchmark tasks under label shift conditions.
Related papers
- Self-training through Classifier Disagreement for Cross-Domain Opinion
Target Extraction [62.41511766918932]
Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining.
Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios.
We propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagrees on the unlabelled target data.
arXiv Detail & Related papers (2023-02-28T16:31:17Z) - Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different.
Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data.
In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
arXiv Detail & Related papers (2022-04-24T12:22:19Z) - Attention-based Cross-Layer Domain Alignment for Unsupervised Domain
Adaptation [14.65316832227658]
Unsupervised domain adaptation (UDA) aims to learn transferable knowledge from a labeled source domain and adapts a trained model to an unlabeled target domain.
One prevailing strategy is to minimize the distribution discrepancy by aligning their semantic features extracted by deep models.
arXiv Detail & Related papers (2022-02-27T08:36:12Z) - Instance Level Affinity-Based Transfer for Unsupervised Domain
Adaptation [74.71931918541748]
We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA.
We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process.
We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
arXiv Detail & Related papers (2021-04-03T01:33:14Z) - Your Classifier can Secretly Suffice Multi-Source Domain Adaptation [72.47706604261992]
Multi-Source Domain Adaptation (MSDA) deals with the transfer of task knowledge from multiple labeled source domains to an unlabeled target domain.
We present a different perspective to MSDA wherein deep models are observed to implicitly align the domains under label supervision.
arXiv Detail & Related papers (2021-03-20T12:44:13Z) - Learning Invariant Representations and Risks for Semi-supervised Domain
Adaptation [109.73983088432364]
We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA)
We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
arXiv Detail & Related papers (2020-10-09T15:42:35Z) - Target Consistency for Domain Adaptation: when Robustness meets
Transferability [8.189696720657247]
Learning Invariant Representations has been successfully applied for reconciling a source and a target domain for Unsupervised Domain Adaptation.
We show that the cluster assumption is violated in the target domain despite being maintained in the source domain.
Our new approach results in a significant improvement, on both image classification and segmentation benchmarks.
arXiv Detail & Related papers (2020-06-25T09:13:00Z) - Implicit Class-Conditioned Domain Alignment for Unsupervised Domain
Adaptation [18.90240379173491]
Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain.
We propose a method that removes the need for explicit optimization of model parameters from pseudo-labels directly.
We present a sampling-based implicit alignment approach, where the sample selection procedure is implicitly guided by the pseudo-labels.
arXiv Detail & Related papers (2020-06-09T00:20:21Z) - Towards Fair Cross-Domain Adaptation via Generative Learning [50.76694500782927]
Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions.
We develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.
arXiv Detail & Related papers (2020-03-04T23:25:09Z)
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.