"Deep Generative Modeling": Introductory Examples

A collection of tools for neural compression enthusiasts.

Curated list of papers and resources focused on neural compression, intended to keep pace with the anticipated surge of research in the recent years.

Bare-bones implementations of some generative models in Jax: diffusion, normalizing flows, consistency models, flow matching, (beta)-VAEs, etc

Official code for "Computationally-Efficient Neural Image Compression with Shallow Decoders", ICCV 2023

A LLaMA2-7b chatbot with memory running on CPU, and optimized using smooth quantization, 4-bit quantization or Intel® Extension For PyTorch with bfloat16.

JPD-SE: High-Level Semantics for Joint Perception-Distortion Enhancement in Image Compression

Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests various encoder-decoder configurations to optimize performance metrics like MSE, SSIM, and PSNR, aiming to achieve near-lossless data compression.

An unofficial replication of NAS Without Training.