no code implementations • 17 Feb 2024 • Hadi M. Dolatabadi, Sarah M. Erfani, Christopher Leckie
Our analysis of these two failure cases of DNNs reveals that finding a unified solution for shortcut learning in DNNs is not out of reach, and TDA can play a significant role in forming such a framework.
1 code implementation • 15 Mar 2023 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
In particular, we leverage the power of diffusion models and show that a carefully designed denoising process can counteract the effectiveness of the data-protecting perturbations.
1 code implementation • 13 Oct 2022 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
We show the effectiveness of the proposed method for robust training of DNNs on various poisoned datasets, reducing the backdoor success rate significantly.
1 code implementation • 13 Sep 2022 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
By leveraging the theory of coreset selection, we show how selecting a small subset of training data provides a principled approach to reducing the time complexity of robust training.
2 code implementations • 1 Dec 2021 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Neural networks are vulnerable to adversarial attacks: adding well-crafted, imperceptible perturbations to their input can modify their output.
1 code implementation • NeurIPS 2020 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Deep learning classifiers are susceptible to well-crafted, imperceptible variations of their inputs, known as adversarial attacks.
1 code implementation • 6 Jul 2020 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Deep neural network classifiers suffer from adversarial vulnerability: well-crafted, unnoticeable changes to the input data can affect the classifier decision.
1 code implementation • 15 Jan 2020 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
The significant advantage of such models is their easy-to-compute inverse.