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 1271–1280 of 4925 papers

Title	Date	Tasks	Status	Score
IBVC: Interpolation-driven B-frame Video Compression	Sep 25, 2023	DecoderMotion Compensation	CodeCode Available	5
Improved Gradient based Adversarial Attacks for Quantized Networks	Mar 30, 2020	image-classificationImage Classification	CodeCode Available	5
Bees Local Phase Quantization Feature Selection for RGB-D Facial Expressions Recognition	Aug 3, 2023	feature selectionQuantization	CodeCode Available	5
Device-friendly Guava fruit and leaf disease detection using deep learning	Sep 26, 2022	DiagnosticModel Optimization	CodeCode Available	5
Development, Optimization, and Deployment of Thermal Forward Vision Systems for Advance Vehicular Applications on Edge Devices	Jan 18, 2023	Model Optimizationobject-detection	CodeCode Available	5
Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on Arm CPUs	Dec 23, 2024	Quantization	CodeCode Available	5
Distilling the Knowledge of Romanian BERTs Using Multiple Teachers	Dec 23, 2021	Dialect IdentificationGPU	CodeCode Available	5
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years	Mar 29, 2018	QuantizationTime Series	CodeCode Available	5
Homology-constrained vector quantization entropy regularizer	Nov 25, 2022	Quantization	CodeCode Available	5
A multimodal dynamical variational autoencoder for audiovisual speech representation learning	May 5, 2023	DenoisingDisentanglement	CodeCode Available	5

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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