[CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
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Updated
Apr 30, 2024 - Python
[CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.
Collection of recent methods on (deep) neural network compression and acceleration.
Efficient Deep Learning Systems course materials (HSE, YSDA)
Code and resources on scalable and efficient Graph Neural Networks
[NeurIPS2022] Official implementation of the paper 'Green Hierarchical Vision Transformer for Masked Image Modeling'.
[CVPR 2024] Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
NeurIPS 2021, Official codes for "Efficient Training of Visual Transformers with Small Datasets".
[NeurIPS 2023] Structural Pruning for Diffusion Models
A list of papers, docs, codes about efficient AIGC. This repo is aimed to provide the info for efficient AIGC research, including language and vision, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.
[ICLR 2022] Data-Efficient Graph Grammar Learning for Molecular Generation
[ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.
Official implementation of "EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS"
Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)
[IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.
Frame Flexible Network (CVPR2023)
Recent Advances on Efficient Vision Transformers
Denoising Diffusion Step-aware Models (ICLR2024)
[Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning
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