SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2051–2075 of 4925 papers

TitleStatusHype
Hyperpoints and Fine Vocabularies for Large-Scale Location Recognition—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Ditto: Accelerating Diffusion Model via Temporal Value Similarity—0
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training—0
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision—0
Distribution-sensitive Information Retention for Accurate Binary Neural Network—0
Binary Neural Network for Speaker Verification—0
Ab-initio quantum chemistry with neural-network wavefunctions—0
Hyperspherical Loss-Aware Ternary Quantization—0
IDKM: Memory Efficient Neural Network Quantization via Implicit, Differentiable k-Means—0
Distribution-Preserving k-Anonymity—0
Distribution-Aware Adaptive Multi-Bit Quantization—0
BinaryBERT: Pushing the Limit of BERT Quantization—0
Distribution Adaptive INT8 Quantization for Training CNNs—0
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement—0
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping—0
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication—0
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation—0
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence—0
New Bounds For Distributed Mean Estimation and Variance Reduction—0
Distributed Mean Estimation with Limited Communication—0
Distributed Learning with Sublinear Communication—0
Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis—0
Distributed Learning with Compressed Gradient Differences—0
Distributed Energy Resource Management: All-Time Resource-Demand Feasibility, Delay-Tolerance, Nonlinearity, and Beyond—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified