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 ⛏️
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)
Compact representations of convolutional neural networks via weight pruning and quantization
Code for our WACV 2021 paper "Exploiting the Redundancy in Convolutional Filters for Parameter Reduction"
Implementation of various neural network pruing methods in pytorch.
ESPN: Extreme Sparse Pruned Network
This repository is for reproducing the results shown in the NNCodec ICML Workshop paper. Additionally, it includes a demo, prepared for the Neural Compression Workshop (NCW).
Image classification using compressed deep neural network ported on resource-constrained platforms.
Neural Network Pruning Using Dependency Measures
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