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 1501–1525 of 4925 papers

TitleStatusHype
Efficient statistical classification of satellite measurementsCode0
CAT: Compression-Aware Training for bandwidth reductionCode0
Federated learning compression designed for lightweight communicationsCode0
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial ExamplesCode0
Automated Cancer Subtyping via Vector Quantization Mutual Information MaximizationCode0
FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal RetrievalCode0
Algorithm and VLSI Design for 1-bit Data Detection in Massive MIMO-OFDMCode0
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
Fast Supervised Discrete Hashing and its AnalysisCode0
Efficiera Residual Networks: Hardware-Friendly Fully Binary Weight with 2-bit Activation Model Achieves Practical ImageNet AccuracyCode0
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep EdgeCode0
Data Upcycling Knowledge Distillation for Image Super-ResolutionCode0
Fast Private Kernel Density Estimation via Locality Sensitive QuantizationCode0
QSGD: Communication-Efficient SGD via Gradient Quantization and EncodingCode0
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary SeminormsCode0
Fast and Slow Gradient Approximation for Binary Neural Network OptimizationCode0
Faster Binary Embeddings for Preserving Euclidean DistancesCode0
QTTNet: Quantized Tensor Train Neural Networks for 3D Object and Video Recognition.Code0
FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNsCode0
FairGLVQ: Fairness in Partition-Based ClassificationCode0
Fast Adjustable Threshold For Uniform Neural Network Quantization (Winning solution of LPIRC-II)Code0
Fast High-Dimensional Bilateral and Nonlocal Means FilteringCode0
AMED: Automatic Mixed-Precision Quantization for Edge DevicesCode0
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAsCode0
Exploring Quantization and Mapping Synergy in Hardware-Aware Deep Neural Network AcceleratorsCode0
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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