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 1171–1180 of 4925 papers

Title	Date	Tasks	Status	Score
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models	Feb 18, 2025	Quantization	CodeCode Available	5
An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2	Nov 15, 2024	Model CompressionQuantization	CodeCode Available	5
Integral Human Pose Regression	Nov 22, 2017	3D Human Pose Estimation3D Pose Estimation	CodeCode Available	5
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation	Apr 20, 2020	Deep LearningMath	CodeCode Available	5
Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks	Oct 8, 2024	QuantizationQuantum Machine Learning	CodeCode Available	5
IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks	Oct 6, 2022	BinarizationQuantization	CodeCode Available	5
Instance-Aware Dynamic Neural Network Quantization	Jan 1, 2022	Quantization	CodeCode Available	5
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models	Nov 10, 2023	In-Context LearningMeta-Learning	CodeCode Available	5
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights	Feb 10, 2017	Quantization	CodeCode Available	5
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching	Jan 1, 2022	Quantization	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