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 751–760 of 4925 papers

Title	Date	Tasks	Status	Hype	Score
Fine-grained Data Distribution Alignment for Post-Training Quantization	Sep 9, 2021	Quantization	CodeCode Available	1	5
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing	Jun 22, 2023	Quantization	CodeCode Available	1	5
Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction	Feb 1, 2022	Neural Network CompressionQuantization	CodeCode Available	1	5
Few shot font generation via transferring similarity guided global style and quantization local style	Sep 2, 2023	DisentanglementFont Generation	CodeCode Available	1	5
Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training	Jun 2, 2023	Quantization	CodeCode Available	1	5
FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation	Jun 13, 2025	Model CompressionQuantization	CodeCode Available	1	5
ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization	Aug 30, 2022	Quantization	CodeCode Available	1	5
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks	Dec 30, 2021	CPUimage-classification	CodeCode Available	1	5
Compression with Bayesian Implicit Neural Representations	May 30, 2023	Audio CompressionQuantization	CodeCode Available	1	5
Quantized Compressed Sensing with Score-based Generative Models	Nov 2, 2022	compressed sensingQuantization	CodeCode Available	1	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