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 4011–4020 of 4925 papers

Title	Date	Tasks	Status	Hype
Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval	Jan 14, 2020	Cross-Modal RetrievalQuantization	—Unverified	0
Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Synthesis and Perspective	Jan 13, 2020	DescriptiveImage Segmentation	—Unverified	0
Deep Optimized Multiple Description Image Coding via Scalar Quantization Learning	Jan 12, 2020	DecoderQuantization	CodeCode Available	0
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers	Jan 12, 2020	ClassificationGeneral Classification	CodeCode Available	0
Embedding Compression with Isotropic Iterative Quantization	Jan 11, 2020	Image RetrievalQuantization	—Unverified	0
Gaussian Approximation of Quantization Error for Estimation from Compressed Data	Jan 9, 2020	compressed sensingQuantization	—Unverified	0
Least squares binary quantization of neural networks	Jan 9, 2020	Quantization	CodeCode Available	1
Resource-Efficient Neural Networks for Embedded Systems	Jan 7, 2020	Autonomous NavigationBIG-bench Machine Learning	—Unverified	0
RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks	Jan 4, 2020	Quantization	—Unverified	0
Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference	Jan 3, 2020	Quantization	CodeCode Available	1

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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