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03/27/2023
Exploring Continual Learning of Diffusion Models
Diffusion models have achieved remarkable success in generating high-qua...
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02/20/2023
Analyzing the Posterior Collapse in Hierarchical Variational Autoencoders
Hierarchical Variational Autoencoders (VAEs) are among the most popular ...
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06/28/2022
Equivariant Priors for Compressed Sensing with Unknown Orientation
In compressed sensing, the goal is to reconstruct the signal from an und...
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05/31/2022
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Diffusion-based Deep Generative Models (DDGMs) offer state-of-the-art pe...
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03/18/2022
Defending Variational Autoencoders from Adversarial Attacks with MCMC
Variational autoencoders (VAEs) are deep generative models used in vario...
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03/10/2021
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks
In this work, we explore adversarial attacks on the Variational Autoenco...
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02/04/2021
CKConv: Continuous Kernel Convolution For Sequential Data
Conventional neural architectures for sequential data present important ...
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08/30/2019
BooVAE: A scalable framework for continual VAE learning under boosting approach
Variational Auto Encoders (VAE) are capable of generating realistic imag...
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08/15/2019
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Automatic segmentation methods based on deep learning have recently demo...
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05/25/2019