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 1021–1030 of 4925 papers

Title	Date	Tasks	Status	Score
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air	Jan 3, 2019	BIG-bench Machine LearningQuantization	CodeCode Available	5
Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer	Jun 20, 2025	EEGQuantization	CodeCode Available	5
Linearly Converging Error Compensated SGD	Oct 23, 2020	Quantization	CodeCode Available	5
Communication Efficient Private Federated Learning Using Dithering	Sep 14, 2023	Federated LearningQuantization	CodeCode Available	5
Communication-Efficient Multi-Device Inference Acceleration for Transformer Models	May 25, 2025	Quantization	CodeCode Available	5
Lightweight Deep Learning Based Channel Estimation for Extremely Large-Scale Massive MIMO Systems	Feb 14, 2024	Quantization	CodeCode Available	5
Light Multi-segment Activation for Model Compression	Jul 16, 2019	Knowledge Distillationmodel	CodeCode Available	5
Communication-Efficient Federated Learning via Predictive Coding	Aug 2, 2021	Data CompressionFederated Learning	CodeCode Available	5
Lightweight Client-Side Chinese/Japanese Morphological Analyzer Based on Online Learning	Aug 1, 2014	Domain AdaptationMorphological Analysis	CodeCode Available	5
Lipschitz Continuity Retained Binary Neural Network	Jul 13, 2022	BinarizationQuantization	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