Vector (and Scalar) Quantization, in Pytorch
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Updated
Aug 15, 2024 - Python
Vector (and Scalar) Quantization, in Pytorch
🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps
ECCV 2022, Oral, VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
⚡ A fast embedded library for approximate nearest neighbor search
Pytorch implementation of stochastically quantized variational autoencoder (SQ-VAE)
Implementation of TiTok, proposed by Bytedance in "An Image is Worth 32 Tokens for Reconstruction and Generation"
Using ideas from product quantization for state-of-the-art neural network compression.
Official PyTorch implementation of QuantArt (CVPR2023)
[official] PyTorch implementation of TimeVQVAE from the paper ["Vector Quantized Time Series Generation with a Bidirectional Prior Model", AISTATS 2023]
Dice.com repo to accompany the dice.com 'Vectors in Search' talk by Simon Hughes, from the Activate 2018 search conference, and the 'Searching with Vectors' talk from Haystack 2019 (US). Builds upon my conceptual search and semantic search work from 2015
Pytorch Implementation of "Neural Discrete Representation Learning"
Implementation of vector quantization algorithms, codes for Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search.
A lightweight efficient audio codec in 30MB with 30~170x compression ratio. Supports 16kHz mono speech audio.
VQ-TensoRF --- Official implementation of our CVPR 2023 paper "Compressing Volumetric Radiance Fields to 1 MB"
This is an official PyTorch implementation of "Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture Generation" (IROS 2022).
A Pytorch Implementations for Various Vector Quantization Methods
A RoQ video playback system for the Sega Dreamcast video game console
PyTorch implementation of VQ-VAE applied on CIFAR10 dataset
A framework for index based similarity search.
PyTorch Lightning implementation of the paper Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. This repository allows to reproduce the main findings of the paper on MNIST and Imagenette datasets.
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