A Collection of Variational Autoencoders (VAE) in PyTorch.
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Updated
May 6, 2024 - Python
A Collection of Variational Autoencoders (VAE) in PyTorch.
Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟
TensorFlow GAN implementation using Gumbel Softmax
Pytorch implementation of stochastically quantized variational autoencoder (SQ-VAE)
Codes for "Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control", ICASSP 2022
Keras implementation of a Variational Auto Encoder with a Concrete Latent Distribution
An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.
Source code for the NAACL 2019 paper "SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression"
Code for "Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection", ECCV 2022
Implementation of NeurIPS 19 paper: Paraphrase Generation with Latent Bag of Words
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
TensorFlow-based implementation of "Attend, Infer, Repeat" paper (Eslami et al., 2016, arXiv:1603.08575).
GAN-Based Text Generation
De novo Drug Design via Binary Representations of SMILES for avoiding the Posterior Collapse Problem (BIBM 2021)
Keras, Tensorflow eager execution implementation of Categorical Variational Autoencoder
Code acompanying the paper Developmentally motivated emergence of compositional communication via template transfer
The implementation of Gumbel softmax reparametrization trick for discrete VAE
Official project of DiverseSampling (ACMMM2022 Paper)
Implementation of the Gumbel-Sigmoid distribution in PyTorch.
Code for TACL 2022 paper on Data-to-text Generation with Variational Sequential Planning
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