June 7, 2024, 4:41 a.m. | Tingxu Han, Weisong Sun, Ziqi Ding, Chunrong Fang, Hanwei Qian, Jiaxun Li, Zhenyu Chen, Xiangyu Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03508v1 Announce Type: new
Abstract: Self-supervised learning (SSL) is increasingly attractive for pre-training encoders without requiring labeled data. Downstream tasks built on top of those pre-trained encoders can achieve nearly state-of-the-art performance. The pre-trained encoders by SSL, however, are vulnerable to backdoor attacks as demonstrated by existing studies. Numerous backdoor mitigation techniques are designed for downstream task models. However, their effectiveness is impaired and limited when adapted to pre-trained encoders, due to the lack of label information when pre-training. To …

abstract art arxiv attacks backdoor cs.ai cs.cr cs.lg data however information performance pre-training self-supervised learning ssl state studies supervised learning tasks training type vulnerable

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