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 4491–4500 of 4925 papers

Title	Date	Tasks	Status
Coreset-Based Neural Network Compression	Jul 25, 2018	Neural Network CompressionQuantization	—Unverified
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction	Jul 24, 2018	Depth EstimationDepth Prediction	CodeCode Available
Performance, Power, and Area Design Trade-offs in Millimeter-Wave Transmitter Beamforming Architectures	Jul 19, 2018	Quantization	CodeCode Available
Hybrid Scene Compression for Visual Localization	Jul 19, 2018	QuantizationSelf-Driving Cars	—Unverified
Statistical Model Compression for Small-Footprint Natural Language Understanding	Jul 19, 2018	Model CompressionNatural Language Understanding	—Unverified
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions	Jul 18, 2018	Quantization	—Unverified
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic	Jul 17, 2018	Quantization	—Unverified
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)	Jul 17, 2018	General ClassificationQuantization	—Unverified
Learning Product Codebooks using Vector Quantized Autoencoders for Image Retrieval	Jul 12, 2018	Image RetrievalQuantization	—Unverified
Deep attention-based classification network for robust depth prediction	Jul 11, 2018	ClassificationDeep Attention	CodeCode Available

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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