Class Conditional Alignment for Partial Domain Adaptation
Adversarial adaptation models have demonstrated significant progress towards transferring knowledge from a labeled source dataset to an unlabeled target dataset. Partial domain adaptation (PDA) investigates the scenarios in which the source domain is large and diverse, and the target label space is a subset of the source label space. The main purpose of PDA is to identify the shared classes between the domains and promote learning transferable knowledge from these classes. In this paper, we propose a multi-class adversarial architecture for PDA. The proposed approach jointly aligns the marginal and class-conditional distributions in the shared label space by minimaxing a novel multi-class adversarial loss function. Furthermore, we incorporate effective regularization terms to encourage selecting the most relevant subset of source domain classes. In the absence of target labels, the proposed approach is able to effectively learn domain-invariant feature representations, which in turn can enhance the classification performance in the target domain. Comprehensive experiments on three benchmark datasets Office-31, Office-Home, and Caltech-Office corroborate the effectiveness of the proposed approach in addressing different partial transfer learning tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationPartial Domain AdaptationTransfer LearningSimilar Papers 제목 키워드 기반
Domain Adaptation and Image Classification via Deep Conditional Adaptation Network
Unsupervised domain adaptation aims to generalize the supervised model trained on a source domain to an unlabeled target domain. Marginal distribution alignment of feature spaces is widely used to reduce the domain discr…
ClassificationDomain AdaptationGeneral Classificationimage-classification+2Robust 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 AdaptationAdversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w.r.t. both $p(x|y)$ and $p(y)$. Since the …
Domain AdaptationUnsupervised Domain AdaptationMaximizing Conditional Independence for Unsupervised Domain Adaptation
Unsupervised domain adaptation studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different distributions. Existing methods mainly focus on matching the marginal distributio…
Domain AdaptationUnsupervised Domain AdaptationUnsupervised Domain Adaptation via Regularized Conditional Alignment
We propose a method for unsupervised domain adaptation that trains a shared embedding to align the joint distributions of inputs (domain) and outputs (classes), making any classifier agnostic to the domain. Joint alignme…
Domain AdaptationUnsupervised Domain Adaptation