This project implements a Regularized Autoencoder (RAE) using backpropagation, inspired by the methodologies described in "The Neural Coding Framework for Learning Generative Models". This repository ...

We introduce a novel learning algorithm, the Curvature-regularized Variational Auto-Encoder (CurvVAE), to achieve this goal. The CurvVAE is able to model the natural variations in human-demonstrated ...

This project implements a Variational Autoencoder (VAE) to generate and reconstruct images ... The VAE model consists of an encoder, a decoder, and a sampling function. The encoder compresses the ...

In VAE, which is an encoder-decoder architecture ... Selecting hyperparameters for a variational autoencoder involves balancing complexity and performance. Tune the latent dimension to capture ...

To address this issue, we propose an importance-weighted sampling enhanced Variational Autoencoder (VAE) approach for the estimation of M3PL and M4PL. The key idea is to adopt a variational inference ...

Latent Representation Learning: The multi-resolution features are fed into the VAE encoder to learn a probabilistic latent ... detection in crowded scenes by synergistically integrating Variational ...