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TARO: Temporal Adversarial Rectification Optimization Using Diffusion Models as Purifiers
Daniel Wesego, Pedram Rooshenas
Adversarial purification with diffusion models seeks to project adversarial examples back toward the data manifold, but balancing semantic preservation and robustness against adapt…
Graph Representation Learning with Diffusion Generative Models
Daniel Wesego
Diffusion models have established themselves as state-of-the-art generative models across various data modalities, including images and videos, due to their ability to accurately a…
Multimodal ELBO with Diffusion Decoders
Daniel Wesego, Pedram Rooshenas
Multimodal variational autoencoders have demonstrated their ability to learn the relationships between different modalities by mapping them into a latent representation. Their desi…
Score-Based Multimodal Autoencoder
Daniel Wesego, Pedram Rooshenas
Multimodal Variational Autoencoders (VAEs) represent a promising group of generative models that facilitate the construction of a tractable posterior within the latent space given…