Vector (and Scalar) Quantization, in Pytorch
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Updated
Sep 4, 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)
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
Implementation of vector quantization algorithms, codes for Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search.
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
PyTorch implementation of VQ-VAE applied on CIFAR10 dataset
A Vector Quantized Masked AutoEncoder for speech emotion recognition
TensorFlow implementation of "Finite Scalar Quantization: VQ-VAE Made Simple" (ICLR 2024)
Train vector quantized CLIP models using pytorch lightning
Non-autoregressive Translation by Learning Target Categorical Codes
Official repository for the paper "Topological Neural Discrete Representation Learning à la Kohonen" (ICML 2023 Workshop on Sampling and Optimization in Discrete Space)
Two lossy algorithms, Discrete Wavelet Transform (DWT) and Vector Quantization (VQ) are added together to compress medical images and see their performance.
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