Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data. They are similar to regular autoencoders, which consist of an encoder and decoder. The encoder takes ...
Batch normalization and dropout for stability Mixed precision training for efficiency Learning rate scheduling and gradient clipping β-VAE support for disentangled representations ...
ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...
Sadria, M. , & Layton, A. . (2023). The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis. bioRxiv ...
Abstract: Variational autoencoders (VAEs) have been widely used for node clustering, with existing methods mainly focusing on enhancing the expressiveness of their latent space. Recently, the ...
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