34 citations · 114 across the 22 of their papers we have counts for
9 papers · 1 filter
Model Inversion Robustness: Can Transfer Learning Help?
Sy-Tuyen Ho, Koh Jun Hao, Keshigeyan Chandrasegaran +2
Model Inversion (MI) attacks aim to reconstruct private training data by abusing access to machine learning models. Contemporary MI attacks have achieved impressive attack performa…
Label-Only Model Inversion Attacks via Knowledge Transfer
Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +1
In a model inversion (MI) attack, an adversary abuses access to a machine learning (ML) model to infer and reconstruct private training data. Remarkable progress has been made in t…
On Measuring Fairness in Generative Models
Christopher T. H. Teo, Milad Abdollahzadeh, Ngai-Man Cheung
Recently, there has been increased interest in fair generative models. In this work, we conduct, for the first time, an in-depth study on fairness measurement, a critical component…
Re-thinking Model Inversion Attacks Against Deep Neural Networks
Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +1
Model inversion (MI) attacks aim to infer and reconstruct private training data by abusing access to a model. MI attacks have raised concerns about the leaking of sensitive informa…
Fair Generative Models via Transfer Learning
Christopher TH Teo, Milad Abdollahzadeh, Ngai-Man Cheung
This work addresses fair generative models. Dataset biases have been a major cause of unfairness in deep generative models. Previous work had proposed to augment large, biased data…
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?
Keshigeyan Chandrasegaran, Ngoc-Trung Tran, Yunqing Zhao +1
This work investigates the compatibility between label smoothing (LS) and knowledge distillation (KD). Contemporary findings addressing this thesis statement take dichotomous stand…
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning
Thilini Cooray, Ngai-Man Cheung
Unsupervised graph-level representation learning plays a crucial role in a variety of tasks such as molecular property prediction and community analysis, especially when data annot…
Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples
Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun +3
Few-shot classifiers have been shown to exhibit promising results in use cases where user-provided labels are scarce. These models are able to learn to predict novel classes simply…
Toward Scalable and Unified Example-based Explanation and Outlier Detection
Penny Chong, Ngai-Man Cheung, Yuval Elovici +1
When neural networks are employed for high-stakes decision-making, it is desirable that they provide explanations for their prediction in order for us to understand the features th…