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 2291–2300 of 4925 papers

Title	Date	Tasks	Status	Hype
Learning Accurate Performance Predictors for Ultrafast Automated Model Compression	Apr 13, 2023	image-classificationImage Classification	CodeCode Available	0
Binary Latent Diffusion	Apr 10, 2023	Image GenerationQuantization	CodeCode Available	1
Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval	Apr 9, 2023	Deep HashingDeep Learning	—Unverified	0
SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers	Apr 8, 2023	Neural Network CompressionQuantization	CodeCode Available	1
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch	Apr 8, 2023	QuantizationSelf-Supervised Learning	CodeCode Available	2
Unsupervised Speech Representation Pooling Using Vector Quantization	Apr 8, 2023	Emotion Recognitionintent-classification	CodeCode Available	0
Benchmarking the Robustness of Quantized Models	Apr 8, 2023	BenchmarkingQuantization	—Unverified	0
Similarity search in the blink of an eye with compressed indices	Apr 7, 2023	Quantization	CodeCode Available	2
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination	Apr 7, 2023	Federated LearningPrivacy Preserving	—Unverified	0
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks	Apr 7, 2023	Neural Architecture SearchQuantization	—Unverified	0

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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