Quantization

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1321–1330 of 4925 papers

Title	Date	Tasks	Status	Score
Harnessing Large Language Models Locally: Empirical Results and Implications for AI PC	May 21, 2025	CPUQuantization	CodeCode Available	5
Guetzli: Perceptually Guided JPEG Encoder	Mar 13, 2017	Perceptual DistanceQuantization	CodeCode Available	5
Denoising Noisy Neural Networks: A Bayesian Approach with Compensation	May 22, 2021	DenoisingQuantization	CodeCode Available	5
GSB: Group Superposition Binarization for Vision Transformer with Limited Training Samples	May 13, 2023	BinarizationKnowledge Distillation	CodeCode Available	5
Loss-aware Weight Quantization of Deep Networks	Feb 23, 2018	BinarizationQuantization	CodeCode Available	5
GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units	Feb 10, 2025	Event-based visionQuantization	CodeCode Available	5
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector Quantization	Apr 16, 2025	Graph LearningGraph Representation Learning	CodeCode Available	5
Goten: GPU-Outsourcing Trusted Execution of Neural Network Training and Prediction	Sep 25, 2019	GPUPrivacy Preserving	CodeCode Available	5
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems	Jun 13, 2023	Edge-computingFederated Learning	CodeCode Available	5
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs	Aug 27, 2024	parameter-efficient fine-tuningQuantization	CodeCode Available	5

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All datasets ImageNet CIFAR-10 Wiki-40B AgeDB-30 CFP-FP COCO (Common Objects in Context)IJB-B IJB-C Knowledge-based:LFW

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	FQ-ViT (ViT-L)	Top-1 Accuracy (%)	85.03	—	Unverified
2	FQ-ViT (ViT-B)	Top-1 Accuracy (%)	83.31	—	Unverified
3	FQ-ViT (Swin-B)	Top-1 Accuracy (%)	82.97	—	Unverified
4	FQ-ViT (Swin-S)	Top-1 Accuracy (%)	82.71	—	Unverified
5	FQ-ViT (DeiT-B)	Top-1 Accuracy (%)	81.2	—	Unverified
6	FQ-ViT (Swin-T)	Top-1 Accuracy (%)	80.51	—	Unverified
7	FQ-ViT (DeiT-S)	Top-1 Accuracy (%)	79.17	—	Unverified
8	Xception W8A8	Top-1 Accuracy (%)	78.97	—	Unverified
9	ADLIK-MO-ResNet50-W4A4	Top-1 Accuracy (%)	77.88	—	Unverified
10	ADLIK-MO-ResNet50-W3A4	Top-1 Accuracy (%)	77.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3DCNN_VIVA_3	MAP	160,327.04	—	Unverified
2	DTQ	MAP	0.79	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OutEffHop-Bert_base	Perplexity	6.3	—	Unverified
2	OutEffHop-Bert_base	Perplexity	6.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		Accuracy	98.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		Accuracy	92.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SSD ResNet50 V1 FPN 640x640	MAP	34.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		TAR @ FAR=1e-4	95.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		TAR @ FAR=1e-4	96.38	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3DCNN_VIVA_5	All	84,809,664	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1		Accuracy	99.8	—	Unverified