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Found 5 papers, 3 papers with code
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
Train-time data poisoning attacks threaten machine learning models by introducing adversarial examples during training, leading to misclassification.
PureEBM: Universal Poison Purification via Mid-Run Dynamics of Energy-Based Models
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 examples during training.
Towards Composable Distributions of Latent Space Augmentations
We propose a composable framework for latent space image augmentation that allows for easy combination of multiple augmentations.
Causal Structural Hypothesis Testing and Data Generation Models
We show that the variational version of the architecture, Causal Structural Variational Hypothesis Testing can improve performance in low SNR regimes.
De-Biasing Generative Models using Counterfactual Methods
Our proposed method combines a causal latent space VAE model with specific modification to emphasize causal fidelity, enabling finer control over the causal layer and the ability to learn a robust intervention framework.
