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 1901–1910 of 4925 papers

Title	Date	Tasks	Status	Hype
Image Compression using only Attention based Neural Networks	Oct 17, 2023	Image CompressionQuantization	—Unverified	0
Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion	Oct 16, 2023	Quantization	—Unverified	0
RoomDesigner: Encoding Anchor-latents for Style-consistent and Shape-compatible Indoor Scene Generation	Oct 16, 2023	QuantizationScene Generation	CodeCode Available	1
One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models	Oct 14, 2023	QuantizationSensitivity	CodeCode Available	0
LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations	Oct 13, 2023	Quantization	—Unverified	0
QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models	Oct 13, 2023	Computational EfficiencyGPU	CodeCode Available	1
Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic Enhancement	Oct 12, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models	Oct 12, 2023	GPUQuantization	CodeCode Available	1
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification	Oct 12, 2023	Federated LearningQuantization	—Unverified	0
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models	Oct 12, 2023	Natural Language UnderstandingQuantization	CodeCode Available	2

Show:10 25 50

← PrevPage 191 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