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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

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Greatest papers with code

Minimum Class Confusion for Versatile Domain Adaptation

ECCV 2020 thuml/Transfer-Learning-Library

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).

MULTI-TARGET DOMAIN ADAPTATION PARTIAL DOMAIN ADAPTATION

Partial Adversarial Domain Adaptation

ECCV 2018 thuml/Transfer-Learning-Library

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.

PARTIAL DOMAIN ADAPTATION

Importance Weighted Adversarial Nets for Partial Domain Adaptation

CVPR 2018 thuml/Transfer-Learning-Library

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.

PARTIAL DOMAIN ADAPTATION TRANSFER LEARNING UNSUPERVISED DOMAIN ADAPTATION

Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

ICML 2020 tim-learn/SHOT

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 DOMAIN ADAPTATION REPRESENTATION LEARNING UNSUPERVISED DOMAIN ADAPTATION

Universal Domain Adaptation through Self Supervision

NeurIPS 2020 VisionLearningGroup/DANCE

While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori.

PARTIAL DOMAIN ADAPTATION UNIVERSAL DOMAIN ADAPTATION UNSUPERVISED DOMAIN ADAPTATION

Learning to Transfer Examples for Partial Domain Adaptation

CVPR 2019 thuml/ETN

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.

PARTIAL DOMAIN ADAPTATION TRANSFER LEARNING

A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation

ECCV 2020 tim-learn/BA3US

On one hand, negative transfer results in misclassification of target samples to the classes only present in the source domain.

PARTIAL DOMAIN ADAPTATION UNSUPERVISED DOMAIN ADAPTATION

Relation Adversarial Network for Low Resource Knowledge Graph Completion

8 Nov 2019zxlzr/RAN

Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations.

KNOWLEDGE GRAPH COMPLETION LINK PREDICTION PARTIAL DOMAIN ADAPTATION RELATION EXTRACTION

Deep Residual Correction Network for Partial Domain Adaptation

10 Apr 2020wenqiwenqi1/DRCN

Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain.

PARTIAL DOMAIN ADAPTATION