A Collection of Variational Autoencoders (VAE) in PyTorch.
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Updated
Jun 13, 2024 - Python
A Collection of Variational Autoencoders (VAE) in PyTorch.
Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟
Pytorch implementation of stochastically quantized variational autoencoder (SQ-VAE)
Source code for the NAACL 2019 paper "SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression"
Implementation of NeurIPS 19 paper: Paraphrase Generation with Latent Bag of Words
Codes for "Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control", ICASSP 2022
Code for "Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection", ECCV 2022
TensorFlow GAN implementation using Gumbel Softmax
An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.
Keras implementation of a Variational Auto Encoder with a Concrete Latent Distribution
TensorFlow-based implementation of "Attend, Infer, Repeat" paper (Eslami et al., 2016, arXiv:1603.08575).
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
Code for TACL 2022 paper on Data-to-text Generation with Variational Sequential Planning
[BMM 24-25] "Just Relax It": Implementation of different relaxation methods
Implementation of the Gumbel-Sigmoid distribution in PyTorch.
Official project of DiverseSampling (ACMMM2022 Paper)
GAN-Based Text Generation
Black-box spike and slab variational inference, example with linear models
De novo Drug Design via Binary Representations of SMILES for avoiding the Posterior Collapse Problem (BIBM 2021)
Python library for the differentiable hypergeometric distribution
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