Accepted to IEEE Robotics and Automation Letters (RA-L) April 2024
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Updated
Apr 5, 2024 - Python
Accepted to IEEE Robotics and Automation Letters (RA-L) April 2024
Compression via Vector Quantization in PyTorch
Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders
State of the art of generative models and in-depth study of diffusion models
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.
Implementation of basic autoencodeur, VAE and VQVAE in Flax
implementation of VQVAE in pytorch
Applying multiple VQ along the feature axis
Official code for the NeurIPS 2022 paper "Posterior Matching for Arbitrary Conditioning".
VQGAN from LDM without hell of dependencies
Image Generation using VQVAE and GPT Models
Tensorflow Implementation of "Theory and Experiments on Vector Quantized Autoencoders"
Large-Scale Bidirectional Training for Zero-Shot Image Captioning
Experimental implementation for a sparse-dictionary based version of the VQ-VAE2 paper
VQ-VAE/GAN implementation in pytorch-lightning
Inverse DALL-E for Optical Character Recognition
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