Papers Partial Domain Adaptation
“Partial Domain Adaptation” 태그가 달린 논문 58편 · 필터 해제
Take It or Leave It: Intent-Controlled Partial Optimal Transport
While optimal transport (OT) enforces a rigid constraint by requiring two measures to be matched exactly, partial optimal transport relaxes this requirement by allowing mass to remain unmatched through a global budget, s…
Partial Domain AdaptationPartial Domain Adaptation via Importance Sampling-based Shift Correction
Partial domain adaptation (PDA) is a challenging task in real-world machine learning scenarios. It aims to transfer knowledge from a labeled source domain to a related unlabeled target domain, where the support set of th…
Partial Domain AdaptationBi-level Unbalanced Optimal Transport for Partial Domain Adaptation
Partial domain adaptation (PDA) problem requires aligning cross-domain samples while distinguishing the outlier classes for accurate knowledge transfer. The widely used weighting framework tries to address the outlier cl…
Domain AdaptationPartial Domain AdaptationTransfer LearningSoft-Masked Semi-Dual Optimal Transport for Partial Domain Adaptation
Visual domain adaptation aims to learn discriminative and domain-invariant representation for an unlabeled target domain by leveraging knowledge from a labeled source domain. Partial domain adaptation (PDA) is a general …
Domain AdaptationPartial Domain AdaptationCo-training partial domain adaptation networks for industrial Fault Diagnosis
The partial domain adaptation (PDA) challenge is a prevalent issue in industrial fault diagnosis. Drawing inspiration from traditional classification settings where such partial challenge is not a concern, we propose a n…
Domain AdaptationFault DiagnosisPartial Domain AdaptationPartial Distribution Matching via Partial Wasserstein Adversarial Networks
This paper studies the problem of distribution matching (DM), which is a fundamental machine learning problem seeking to robustly align two probability distributions. Our approach is established on a relaxed formulation,…
Domain AdaptationPartial Domain AdaptationLearning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach
Domain adaptation has shown appealing performance by leveraging knowledge from a source domain with rich annotations. However, for a specific target task, it is cumbersome to collect related and high-quality source domai…
Domain AdaptationPartial Domain AdaptationA Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting
Despite the progress made in domain adaptation, solving Unsupervised Domain Adaptation (UDA) problems with a general method under complex conditions caused by label shifts between domains remains a formidable task. In th…
Domain AdaptationPartial Domain AdaptationUniversal Domain AdaptationUnsupervised Domain AdaptationRobust Class-Conditional Distribution Alignment for Partial Domain Adaptation
Unwanted samples from private source categories in the learning objective of a partial domain adaptation setup can lead to negative transfer and reduce classification performance. Existing methods, such as re-weighting o…
Domain AdaptationPartial Domain AdaptationA Robust Negative Learning Approach to Partial Domain Adaptation Using Source Prototypes
This work proposes a robust Partial Domain Adaptation (PDA) framework that mitigates the negative transfer problem by incorporating a robust target-supervision strategy. It leverages ensemble learning and includes divers…
Domain AdaptationEnsemble LearningPartial Domain AdaptationPseudo LabelMOT: Masked Optimal Transport for Partial Domain Adaptation
As an important methodology to measure distribution discrepancy, optimal transport (OT) has been successfully applied to learn generalizable visual models under changing environments. However, there are still limitat…
Domain AdaptationPartial Domain AdaptationDomain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation
The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. However, in realistic scenarios, finding an …
Domain Adaptationdomain classificationPartial Domain AdaptationTransfer Learning+1A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
Unsupervised Domain Adaptation (UDA) aims at classifying unlabeled target images leveraging source labeled ones. In this work, we consider the Partial Domain Adaptation (PDA) variant, where we have extra source classes n…
Domain AdaptationModel SelectionPartial Domain AdaptationUnsupervised Domain AdaptationSelective Partial Domain Adaptation
Partial Domain Adaptation (PDA), which assumes that the label space of the target domain is a subset of that in the source domain, has attracted much attention in recent years. Due to the difference in the label space of…
Domain AdaptationPartial Domain AdaptationCoupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation
In contrast to a standard closed-set domain adaptation task, partial domain adaptation setup caters to a realistic scenario by relaxing the identical label set assumption. The fact of source label set subsuming the targe…
Domain Adaptationdomain classificationPartial Domain AdaptationTransfer LearningOneRing: A Simple Method for Source-free Open-partial Domain Adaptation
In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-OPDA set…
Domain AdaptationDomain GeneralizationOpen Set LearningPartial Domain Adaptation+1Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation
New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. While several domain adaptation approache…
Domain AdaptationPartial Domain AdaptationFrom Big to Small: Adaptive Learning to Partial-Set Domains
Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirement of identical class space shared acro…
Domain AdaptationPartial Domain AdaptationLow-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling efficient CNN retraining on edge devices
With the increased deployment of Convolutional Neural Networks (CNNs) on edge devices, the uncertainty of the observed data distribution upon deployment has led researchers to to utilise large and extensive datasets such…
Domain AdaptationPartial Domain AdaptationImplicit Semantic Response Alignment for Partial Domain Adaptation
Partial Domain Adaptation (PDA) addresses the unsupervised domain adaptation problem where the target label space is a subset of the source label space. Most state-of-art PDA methods tackle the inconsistent label space b…
Domain AdaptationPartial Domain AdaptationUnsupervised Domain Adaptation