Vaepy

Vaepy is a Python library designed for variational autoencoders (VAEs). VAEs are a class of generative models that learn a probabilistic mapping from a latent space to a data space. They consist of an encoder that maps input data to a distribution in the latent space and a decoder that samples from this distribution to reconstruct the data. Vaepy provides tools and building blocks for constructing, training, and evaluating VAEs.

The library aims to simplify the implementation of VAEs by offering pre-built components for common VAE architectures, loss functions, and sampling strategies. Users can leverage vaepy to experiment with different VAE configurations for tasks such as image generation, anomaly detection, and representation learning. The core functionality typically involves defining the neural network architectures for the encoder and decoder, specifying the prior and posterior distributions of the latent variables, and implementing the VAE objective function, which balances reconstruction accuracy with the regularization of the latent space.

Vaepy can also facilitate hyperparameter tuning and model evaluation, providing metrics to assess the quality of generated samples and the learned latent representation. Its modular design allows for customization, enabling researchers and practitioners to integrate their own custom layers or loss functions. The library is often used in conjunction with other deep learning frameworks like TensorFlow or PyTorch, acting as a higher-level abstraction for VAE-specific operations.