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

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Showing 3601–3625 of 4925 papers

TitleStatusHype
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning—0
Robustness and Transferability of Universal Attacks on Compressed ModelsCode1
Recurrence of Optimum for Training Weight and Activation Quantized Networks—0
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework—0
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses—0
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer—0
Design and Analysis of Uplink and Downlink Communications for Federated Learning—0
Parallel Blockwise Knowledge Distillation for Deep Neural Network CompressionCode0
Going Beyond Classification Accuracy Metrics in Model CompressionCode1
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning—0
Edge Deep Learning for Neural Implants—0
Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architectureCode0
Communication-Efficient Federated Distillation—0
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary SeminormsCode0
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory—0
Ultra-Low Precision 4-bit Training of Deep Neural Networks—0
Using dynamical quantization to perform split attempts in online tree regressors—0
KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and QuantizationCode1
Training and Inference for Integer-Based Semantic Segmentation Network—0
A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints—0
FactorizeNet: Progressive Depth Factorization for Efficient Network Architecture Exploration Under Quantization Constraints—0
Where Should We Begin? A Low-Level Exploration of Weight Initialization Impact on Quantized Behaviour of Deep Neural Networks—0
Reconstruction Condition of Quantized Signals in Unlimited Sampling Framework—0
Fully Quantized Image Super-Resolution NetworksCode1
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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