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 4201–4225 of 4925 papers

TitleStatusHype
NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform QuantizationCode0
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural NetworksCode0
Learn to Compress CSI and Allocate Resources in Vehicular Networks—0
Unsupervised Neural Quantization for Compressed-Domain Similarity SearchCode0
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations—0
Primary quantization matrix estimation of double compressed JPEG images via CNNCode0
Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge—0
GDRQ: Group-based Distribution Reshaping for Quantization—0
U-Net Fixed-Point Quantization for Medical Image SegmentationCode0
Efficient computation of counterfactual explanations of LVQ modelsCode0
Deep Task-Based Quantization—0
Central Similarity Quantization for Efficient Image and Video RetrievalCode0
Learn to Allocate Resources in Vehicular Networks—0
DeepCABAC: A Universal Compression Algorithm for Deep Neural NetworksCode0
Robust and Communication-Efficient Collaborative LearningCode0
QRMODA and BRMODA: Novel Models for Face Recognition Accuracy in Computer Vision Systems with Adapted Video Streams—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Splitting—0
Exploring Semantic Segmentation on the DCT Representation—0
Light Multi-segment Activation for Model CompressionCode0
An Inter-Layer Weight Prediction and Quantization for Deep Neural Networks based on a Smoothly Varying Weight Hypothesis—0
Learning Multimodal Fixed-Point Weights using Gradient Descent—0
The Bach Doodle: Approachable music composition with machine learning at scale—0
And the Bit Goes Down: Revisiting the Quantization of Neural NetworksCode1
A Targeted Acceleration and Compression Framework for Low bit Neural Networks—0
Multi-Scale Vector Quantization with Reconstruction Trees—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