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 ...
Abstract: In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models ...
Batch normalization and dropout for stability Mixed precision training for efficiency Learning rate scheduling and gradient clipping β-VAE support for disentangled representations ...
Este repositório reúne o código, os slides e o relatório desenvolvidos para o trabalho da disciplina de Redes Neurais (NES), cujo objetivo foi comparar o comportamento de um Autoencoder clássico com ...
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 ...
Abstract: Creating a comprehensively representative image while maintaining the merits of various modalities is a key focus of current Multi-Modality Image Fusion research. Existing unified methods ...
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