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Quantization

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하위 태스크: 2
평가표: 10
보존된 평가 기록: 32

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Orange-OpenSource/AIVC

Conditional Coding and Variable Bitrate for Practical Learned Video Coding

https://github.com/PaddlePaddle/FleetX

ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation

https://github.com/PaddlePaddle/PaddleDetection

YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/pipelines/examples/text_to_image

ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation

https://github.com/PaddlePaddle/PaddleOCR

PACT: Parameterized Clipping Activation for Quantized Neural Networks

https://github.com/PaddlePaddle/PaddleYOLO

YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

https://github.com/PanQiWei/AutoGPTQ

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

https://github.com/PannenetsF/TQT

Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks

https://github.com/PannenetsF/wageubn

Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers

https://github.com/PhilipBAdams/faiss-learned-termination-prior-weighted

Polysemous codes

https://github.com/PhilipPfeffer/haptic_vest

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/ARMnn_Quantize_RPi_32-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_Classification_Jetson-Nano

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_Classification_RPi_32-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_Classification_RPi_64-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_Classification_RPi_zero

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_RPi_32-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_RPi_64-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qualcomm-AI-research/BayesianBits

Bayesian Bits: Unifying Quantization and Pruning

https://github.com/QwenLM/Qwen-7B

QLoRA: Efficient Finetuning of Quantized LLMs

https://github.com/Rain9876/ShareLoRA

QLoRA: Efficient Finetuning of Quantized LLMs

https://github.com/Rayyan-Akhtar/XLSR

Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices

https://github.com/RitAreaSciencePark/ZigZagLLMs

The Unreasonable Ineffectiveness of the Deeper Layers

https://github.com/SarangShaikh201/fastText

FastText.zip: Compressing text classification models

https://github.com/SauravMaheshkar/Compressed-DNNs-Forget

What Do Compressed Deep Neural Networks Forget?

https://github.com/SenJia/Farthest-Neighbor-AUC

Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve

https://github.com/SeyedMuhammadHosseinMousavi/Bees-Local-Phase-Quantization-Feature-Selection-for-RGB-D-Facial-Expressions-Recognition

Bees Local Phase Quantization Feature Selection for RGB-D Facial Expressions Recognition

https://github.com/ShunLu91/Single-Path-One-Shot-NAS

Single Path One-Shot Neural Architecture Search with Uniform Sampling

https://github.com/Shunli-Wang/Tiny-YOLO-LSQ

Learned Step Size Quantization

https://github.com/Simon-lsy/Deep_Compression

Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

https://github.com/TAMU-VITA/AGD

GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework

https://github.com/THUDM/GLM

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/TuSimple/RangeDet

RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection

https://github.com/UMich-BipedLab/AprilTag_ROS

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/ROB101-ExtrinsicCalibrationProblem

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/automatic_lidar_camera_calibration

Improvements to Target-Based 3D LiDAR to Camera Calibration

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