PyTorch VAE

This project is a deep learning research toolkit and generative model library providing implementations of Variational Autoencoders using the PyTorch framework. It serves as a framework for training and evaluating autoencoder architectures to learn latent representations for data reconstruction and the generation of synthetic data samples.

The toolkit focuses on unsupervised feature learning and generative model training, featuring a system for mapping external configuration files to model hyperparameters to ensure reproducible experimental runs. It includes mechanisms for tracking training progress and monitoring model performance through the visualization of output samples and latent space organization.

The implementation covers core generative architecture components, including encoder-decoder symmetry, latent space bottlenecks, and the reparameterization trick for gradient-based learning. It also utilizes KL-divergence regularization to constrain learned latent distributions.

Features

Deep Learning Toolkits - Functions as a deep learning toolkit specifically designed for training and evaluating autoencoder experiments.

Reparameterization Trick - Implements the reparameterization trick to enable gradient backpropagation through stochastic nodes in the VAE.

Symmetric Encoder-Decoders - Implements mirrored encoder and decoder paths to facilitate efficient feature extraction and data reconstruction.

Latent Space Compression - Employs a low-dimensional latent space bottleneck to force the model to learn compressed representations of data.

Generative Models - Provides a library of generative models that learn data distributions to produce synthetic samples from latent spaces.

Generative Model Training Tools - Includes a complete pipeline for training Variational Autoencoders to learn representations for data generation.

Variational Autoencoders - Implements Variational Autoencoder models that map input data to continuous latent distributions for reconstruction.

PyTorch Implementations - Offers multiple Variational Autoencoder architectures implemented specifically within the PyTorch framework.

Unsupervised Learning - Implements unsupervised feature learning using autoencoder architectures to discover patterns in unlabeled datasets.

KL-Divergence Penalties - Utilizes KL-divergence penalties in the loss function to regularize the learned latent distribution against a prior.

Deep Learning Research - Provides a research-oriented environment for conducting experiments with generative neural network architectures.

Hyperparameter Configurations - Provides a system for managing model hyperparameters via external configuration files to ensure experimental reproducibility.

Training Progress Monitoring - Ships a logging mechanism to track training metrics and output samples for external performance monitoring.

Modular Architectures - Uses a modular design with separate PyTorch modules for encoders and decoders to allow interchangeable network backbones.

Training Configuration Systems - Features a configuration system for externalizing hyperparameters, data paths, and hardware settings for reproducible training.

Latent Space Manifold Visualization - Provides mechanisms to visualize output samples and the organization of the latent space during model training.

AntixKPyTorch-VAE

Features

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