A Sample Selection Approach for Universal Domain Adaptation

01/14/2020
by   Omri Lifshitz, et al.
0

We study the problem of unsupervised domain adaption in the universal scenario, in which only some of the classes are shared between the source and target domains. We present a scoring scheme that is effective in identifying the samples of the shared classes. The score is used to select which samples in the target domain to pseudo-label during training. Another loss term encourages diversity of labels within each batch. Taken together, our method is shown to outperform, by a sizable margin, the current state of the art on the literature benchmarks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/10/2020

Unveiling Class-Labeling Structure for Universal Domain Adaptation

As a more practical setting for unsupervised domain adaptation, Universa...
research
12/18/2018

TWINs: Two Weighted Inconsistency-reduced Networks for Partial Domain Adaptation

The task of unsupervised domain adaptation is proposed to transfer the k...
research
11/02/2022

An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction

Learning-based approaches to modeling crowd motion have become increasin...
research
10/25/2021

Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation

Domain adaptation solves image classification problems in the target dom...
research
06/14/2022

Confidence Score for Source-Free Unsupervised Domain Adaptation

Source-free unsupervised domain adaptation (SFUDA) aims to obtain high p...
research
09/18/2021

S^3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation

Unsupervised domain adaptation (DA) methods have focused on achieving ma...
research
10/08/2020

Distributionally Robust Learning for Unsupervised Domain Adaptation

We propose a distributionally robust learning (DRL) method for unsupervi...

Please sign up or login with your details

Forgot password? Click here to reset