1 citations · 1 across the 6 of their papers we have counts for
6 papers
PUREVQ-GAN: Defending Data Poisoning Attacks through Vector-Quantized Bottlenecks
Alexander Branch, Omead Pooladzandi, Radin Khosraviani +3
We introduce PureVQ-GAN, a defense against data poisoning that forces backdoor triggers through a discrete bottleneck using Vector-Quantized VAE with GAN discriminator. By quantizi…
PureEBM: Universal Poison Purification via Mid-Run Dynamics of Energy-Based Models
Omead Pooladzandi, Jeffrey Jiang, Sunay Bhat +1
Data poisoning attacks pose a significant threat to the integrity of machine learning models by leading to misclassification of target distribution data by injecting adversarial ex…
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
Sunay Bhat, Jeffrey Jiang, Omead Pooladzandi +2
Train-time data poisoning attacks threaten machine learning models by introducing adversarial examples during training, leading to misclassification. Current defense methods often…
Towards Composable Distributions of Latent Space Augmentations
Omead Pooladzandi, Jeffrey Jiang, Sunay Bhat +1
We propose a composable framework for latent space image augmentation that allows for easy combination of multiple augmentations. Image augmentation has been shown to be an effecti…
Causal Structural Hypothesis Testing and Data Generation Models
Jeffrey Jiang, Omead Pooladzandi, Sunay Bhat +1
A vast amount of expert and domain knowledge is captured by causal structural priors, yet there has been little research on testing such priors for generalization and data synthesi…
De-Biasing Generative Models using Counterfactual Methods
Sunay Bhat, Jeffrey Jiang, Omead Pooladzandi +1
Variational autoencoders (VAEs) and other generative methods have garnered growing interest not just for their generative properties but also for the ability to dis-entangle a low-…