The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning

1 November 2022

Papers citing "The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning"

8 / 8 papers shown

Title
Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy Rushabh Solanki Meghana Bhange Ulrich Aïvodji Elliot Creager 39 0 0 09 May 2025
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks Lukas Gosch Mahalakshmi Sabanayagam D. Ghoshdastidar Stephan Günnemann AAML 45 3 0 15 Jul 2024
Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference Yujin Han Difan Zou AAML 57 3 0 22 Apr 2024
Pruning the Unlabeled Data to Improve Semi-Supervised Learning Guy Hacohen D. Weinshall SSL 26 1 0 27 Aug 2023
A Pathway Towards Responsible AI Generated Content Chen Chen Jie Fu Lingjuan Lyu 49 71 0 02 Mar 2023
Rethinking Backdoor Data Poisoning Attacks in the Context of Semi-Supervised Learning Marissa Connor Vincent Emanuele SILM AAML 33 1 0 05 Dec 2022
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 279 868 0 15 Oct 2021
Poisoning the Unlabeled Dataset of Semi-Supervised Learning Nicholas Carlini AAML 166 68 0 04 May 2021