Universal Source-Free Domain Adaptation

04/09/2020
by   Jogendra Nath Kundu, et al.
26

There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift. Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA). Furthermore, almost all prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for real-time adaptation. Devoid of such impractical assumptions, we propose a novel two-stage learning process. 1) In the Procurement stage, we aim to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift. To achieve this, we enhance the model's ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework. 2) In the Deployment stage, the goal is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples. To this end, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighting mechanism, named as Source Similarity Metric (SSM). A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/26/2023

Universal Domain Adaptation for Remote Sensing Image Scene Classification

The domain adaptation (DA) approaches available to date are usually not ...
research
12/16/2021

UMAD: Universal Model Adaptation under Domain and Category Shift

Learning to reject unknown samples (not present in the source classes) i...
research
04/09/2020

Towards Inheritable Models for Open-Set Domain Adaptation

There has been a tremendous progress in Domain Adaptation (DA) for visua...
research
12/08/2019

Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation

Domain Adaptation (DA) transfers a learning model from a labeled source ...
research
05/26/2020

Keep it Simple: Image Statistics Matching for Domain Adaptation

Applying an object detector, which is neither trained nor fine-tuned on ...
research
10/28/2022

Subsidiary Prototype Alignment for Universal Domain Adaptation

Universal Domain Adaptation (UniDA) deals with the problem of knowledge ...
research
06/16/2022

Balancing Discriminability and Transferability for Source-Free Domain Adaptation

Conventional domain adaptation (DA) techniques aim to improve domain tra...

Please sign up or login with your details

Forgot password? Click here to reset