Data compression in TensorFlow
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Updated
Aug 7, 2024 - Python
Data compression in TensorFlow
Pure python implementation of product quantization for nearest neighbor search
Data Compression using Arithmetic Encoding in Python
The implementation of various data compression techniques.
Compression suite for data frames and tabular data files, csv, excel etc. Using LZHW algorithm.
A lightweight rANSCoder meant for rapid prototyping.
A large compression model for weather and climate data, which compresses a 400+ TB ERA5 dataset into a new 0.8 TB CRA5 dataset.
A parallel implementation of the bzip2 data compressor in python, this data compression pipeline is using algorithms like Burrows–Wheeler transform (BWT) and Move to front (MTF) to improve the Huffman compression. For now, this tool only will be focused on compressing .csv files, and other files on tabular format.
监控在应用程序中的键盘活动,并自动记录定时通过邮件发送 / Monitor keyboard activities in applications, automatically recording and sending logs regularly via email
Caffe/Neon prototxt training file for our Neurocomputing2017 work: Fuzzy Quantitative Deep Compression Network
[Journal of Turbulence, DCC 2022] Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers
This library helps you compress your dataset to optimize Ai Training costs.
we can compress and decompress text data or image with this project. This project is created in python with LZW-data-compresstion -decompresstion algorithom
Reduced Compressed Description for Direct Electron Microscopy Data
Sketching algorithms for Tensor Train decompositions
[DCC 2020] DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
Autoencoders are neural networks used for data compression, image de-noising, and dimensionality reduction. Using PyTorch.
A simple encoder/decoder multi-tool for qr code data compression that utilizes gzip, base64 and hex.
Explore our extensive Algorithms Repository, featuring a diverse range of algorithms from computational methods to data structures, cryptographic techniques, and AI. Ideal for education, research, and practical application, each algorithm is well-documented, tested, and optimized for performance and readability.
A Video Compression Autoencoder using Volumetric Convolution
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