Partial Domain Adaptation
20 papers with code • 5 benchmarks • 4 datasets
Partial Domain Adaptation is a transfer learning paradigm, which manages to transfer relevant knowledge from a large-scale source domain to a small-scale target domain.
Source: Deep Residual Correction Network for Partial Domain Adaptation
Libraries
Use these libraries to find Partial Domain Adaptation models and implementations
Office-Home
DomainNet
Office-31
VisDA-2017
Most implemented papers
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions.
Minimum Class Confusion for Versatile Domain Adaptation
thuml/Versatile-Domain-Adaptation
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ECCV 2020
It can be characterized as (1) a non-adversarial DA method without explicitly deploying domain alignment, enjoying faster convergence speed; (2) a versatile approach that can handle four existing scenarios: Closed-Set, Partial-Set, Multi-Source, and Multi-Target DA, outperforming the state-of-the-art methods in these scenarios, especially on one of the largest and hardest datasets to date (7. 3% on DomainNet).
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain.
Partial Adversarial Domain Adaptation
thuml/PADA
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ECCV 2018
We present Partial Adversarial Domain Adaptation (PADA), which simultaneously alleviates negative transfer by down-weighing the data of outlier source classes for training both source classifier and domain adversary, and promotes positive transfer by matching the feature distributions in the shared label space.
Improving Mini-batch Optimal Transport via Partial Transportation
ut-austin-data-science-group/mini-batch-ot
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Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory issue of OT in large-scale applications.
Selective Partial Domain Adaptation
Pengxin-Guo/SPDA
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British Machine Vision Conference 2022
To solve this problem, we propose a Selective Partial Domain Adaptation (SPDA) method, which selects useful data for the adaptation to the target domain.
Importance Weighted Adversarial Nets for Partial Domain Adaptation
thuml/Transfer-Learning-Library
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CVPR 2018
This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain.
Learning to Transfer Examples for Partial Domain Adaptation
Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer.
Universal Domain Adaptation through Self Supervision
VisionLearningGroup/DANCE
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NeurIPS 2020
While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori.
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
On one hand, negative transfer results in misclassification of target samples to the classes only present in the source domain.
