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 1141–1150 of 4925 papers

Title	Date	Tasks	Status	Score
Learning Compression from Limited Unlabeled Data	Sep 1, 2018	CPUGPU	CodeCode Available	5
KP2Dtiny: Quantized Neural Keypoint Detection and Description on the Edge	Jul 7, 2023	Homography EstimationKeypoint Detection	CodeCode Available	5
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion	Oct 14, 2022	Deep Reinforcement LearningQuantization	CodeCode Available	5
JPEG Inspired Deep Learning	Oct 9, 2024	Deep LearningFine-Grained Image Classification	CodeCode Available	5
Joint Maximum Purity Forest with Application to Image Super-Resolution	Aug 30, 2017	ClusteringGeneral Classification	CodeCode Available	5
BRIDLE: Generalized Self-supervised Learning with Quantization	Feb 4, 2025	image-classificationImage Classification	CodeCode Available	5
Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks	Jul 1, 2024	Quantization	CodeCode Available	5
KVTuner: Sensitivity-Aware Layer-wise Mixed Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference	Feb 6, 2025	Mathematical ReasoningQuantization	CodeCode Available	5
Forward and Backward Information Retention for Accurate Binary Neural Networks	Sep 24, 2019	BinarizationNeural Network Compression	CodeCode Available	5
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models	Feb 18, 2025	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