Tensorflow Implementation of "Theory and Experiments on Vector Quantized Autoencoders"
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Updated
Feb 27, 2019 - Python
Tensorflow Implementation of "Theory and Experiments on Vector Quantized Autoencoders"
Voice conversion (VC) investigation using three variants of VAE
Applying multiple VQ along the feature axis
implementation of VQVAE in pytorch
An educational project dedicated to text-to-image generation with neural networks. VQVAE and BPE autoencoders are used to learn the embedding of text and image respectively. A transformer-based model then is trained to predict the next token in the concatenated sequence of image and text tokens and used for generation.
A toolkit for non-parallel voice conversion based on vector-quantized variational autoencoder
Inverse DALL-E for Optical Character Recognition
Language Quantized AutoEncoders
Large-Scale Bidirectional Training for Zero-Shot Image Captioning
Official code for the NeurIPS 2022 paper "Posterior Matching for Arbitrary Conditioning".
Image Generation using VQVAE and GPT Models
Source code for "Taming Visually Guided Sound Generation" (Oral at the BMVC 2021)
Fast and scalable search of whole-slide images via self-supervised deep learning - Nature Biomedical Engineering
State of the art of generative models and in-depth study of diffusion models
Experimental implementation for a sparse-dictionary based version of the VQ-VAE2 paper
Demo of robust semantic communication against semantic noise
Implementation of basic autoencodeur, VAE and VQVAE in Flax
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