neural-network-compression

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

Embedded and mobile deep learning research resources

Using Teacher Assistants to Improve Knowledge Distillation: https://arxiv.org/pdf/1902.03393.pdf

[ICML 2018] "Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions"

Tools and libraries to run neural networks in Minecraft ⛏️

TinyML & Edge AI: On-device inference, model quantization, embedded ML, ultra-low-power AI for microcontrollers and IoT devices.

This repository contains code to replicate the experiments given in NeurIPS 2019 paper "One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers"

Hyperspectral CNN compression and band selection

[ICLR 2022] "Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable", by Shaojin Ding, Tianlong Chen, Zhangyang Wang

Official PyTorch implementation of "LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging" (ICML 2024)

[ICLR 2023] Pruning Deep Neural Networks from a Sparsity Perspective

Bayesian Optimization-Based Global Optimal Rank Selection for Compression of Convolutional Neural Networks, IEEE Access

Code for testing DCT plus Sparse (DCTpS) networks

Official PyTorch implementation of "Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming" (ICML'23)

Code for our WACV 2021 paper "Exploiting the Redundancy in Convolutional Filters for Parameter Reduction"

Compact representations of convolutional neural networks via weight pruning and quantization

Fraunhofer HHI implementation of the Neural Network Coding (NNC) Standard

Neural network compression with SVD

Implementation of various neural network pruing methods in pytorch.