Search Results for author:
Found 3 papers, 1 papers with code
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
