Echo-cwb's repositories

DeepSC

Pytorch implementation of the DeepSC

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SSCC

Traditional Codecs of Compression, also with Channel Coding

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VQ-Diffusion

Official implementation of VQ-Diffusion

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awesome-chatgpt-prompts-zh

ChatGPT 中文调教指南。各种场景使用指南。学习怎么让它听你的话。

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Checkerboard-Context-Model-Pytorch

An unofficial pytorch implementation of CVPR2021 paper "Checkerboard Context Model for Efficient Learned Image Compression".

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CodeFormer

[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer

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CompressAI_cwb

A PyTorch library and evaluation platform for end-to-end compression research

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CVQN

The code for Channel-Level Variable Quantization Network for Deep Image Compression (IJCAI 2020)

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dalle-mini

DALL·E Mini - Generate images from a text prompt

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deep-image-compression

Lossy image compression using neural networks

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DynamicVectorQuantization

Official Pytorch Implementation of Our CVPR2023 Paper: "Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector Quantization"

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enhancing-transformers

An unofficial implementation of both ViT-VQGAN and RQ-VAE in Pytorch

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high-fidelity-generative-compression

Pytorch implementation of High-Fidelity Generative Image Compression + Routines for neural image compression

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imgcomp-cvpr

TensorFlow implementation of Conditional Probability Models for Deep Image Compression, published in CVPR 2018

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MoVQGAN

MoVQGAN - model for the image encoding and reconstruction

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OFDM-guided-JSCC

Code for paper 'OFDM-guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels', IEEE TCCN

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OptiCommPy

Simulate optical communications systems with Python.

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rq-vae-transformer

The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)

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taming-transformers

Taming Transformers for High-Resolution Image Synthesis

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TinyLIC

High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation

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universal-dic

Official implementation of "Universal Deep Image Compression via Content-Adaptive Optimization with Adapters" presented at WACV 23 (https://arxiv.org/abs/2211.00918).

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