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 731–740 of 4925 papers

Title	Date	Tasks	Status	Hype
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization	Oct 4, 2024	Deep Reinforcement LearningQuantization	CodeCode Available	1
Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance	Mar 16, 2022	GPUQuantization	CodeCode Available	1
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution Networks	May 10, 2023	QuantizationSuper-Resolution	CodeCode Available	1
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study	Jul 16, 2023	In-Context LearningInstruction Following	CodeCode Available	1
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation	Feb 21, 2024	Arithmetic ReasoningGSM8K	CodeCode Available	1
Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression	Feb 23, 2025	Efficient Neural NetworkQuantization	CodeCode Available	1
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings	Apr 1, 2022	Contrastive LearningKnowledge Distillation	CodeCode Available	1
CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution	Jul 21, 2022	Image Super-ResolutionQuantization	CodeCode Available	1
CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space	Jul 16, 2022	6D Pose EstimationCausal Inference	CodeCode Available	1
Quantized Compressed Sensing with Score-based Generative Models	Nov 2, 2022	compressed sensingQuantization	CodeCode Available	1

Show:10 25 50

← PrevPage 74 of 493Next →

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