March 14, 2024, 4:47 a.m. | Idit Diamant, Amir Rosenfeld, Idan Achituve, Jacob Goldberger, Arnon Netzer

cs.CV updates on

arXiv:2401.01650v2 Announce Type: replace
Abstract: Source-free domain adaptation (SFDA) aims to adapt a source-trained model to an unlabeled target domain without access to the source data. SFDA has attracted growing attention in recent years, where existing approaches focus on self-training that usually includes pseudo-labeling techniques. In this paper, we introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings and learn to de-confuse the pseudo-labels. More specifically, we learn a noise transition matrix of the pseudo-labels …

abstract adapt arxiv attention data domain domain adaptation focus free labeling labels noise novel paper self-training source data training type

Senior Data Engineer

@ Displate | Warsaw

Junior Data Analyst - ESG Data

@ Institutional Shareholder Services | Mumbai

Intern Data Driven Development in Sensor Fusion for Autonomous Driving (f/m/x)

@ BMW Group | Munich, DE

Senior MLOps Engineer, Machine Learning Platform

@ GetYourGuide | Berlin

Data Engineer, Analytics

@ Meta | Menlo Park, CA

Data Engineer

@ Meta | Menlo Park, CA