PyTorch models optimization by neural network pruning

basis embedding: a product quantization based model compression method for language models.

Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning".

Industry 4.0 collaborations with Control2K, for using AI on IOT devices to analyse factory machinery

deep learning model compression with pruning

Versioning System for Online Learning systems (VSOL)

Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

Pruning System in Keras for a Deeper Look Into Convolutions

模型剪枝小demo

Presented at the 2023 International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2023). Lightweight mirror segmentation CNN that uses an EfficientNet backbone, employs parallel convolutional layers to capture edge features, and applies filter pruning for model compression

Code for “Discrimination-aware-Channel-Pruning-for-Deep-Neural-Networks”

Model Compression CLI Tool for Keras.

A flexible utility for converting tensor precision in PyTorch models and safetensors files, enabling efficient deployment across various platforms.

Compressed CNNs for airplane classification in satellite images (APoZ-based parameter pruning, INT8 weight quantization)

Brute Force Architecture Search

[ICLR 2022] Code for paper "Exploring Extreme Parameter Compression for Pre-trained Language Models"(https://arxiv.org/abs/2205.10036)

Model Compression using Knowledge Distillation

About Code for the paper "NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models" (EMNLP 2023 Findings)

[NeurIPS 2024] BLAST: Block Level Adaptive Structured Matrix for Efficient Deep Neural Network Inference

Neural network compression with SVD