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 2851–2875 of 4925 papers

TitleStatusHype
Communication-Efficient Adaptive Federated LearningCode1
MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators—0
BMInf: An Efficient Toolkit for Big Model Inference and TuningCode2
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Deep Geometry Post-Processing for Decompressed Point CloudsCode1
RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training QuantizationCode1
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization—0
Improving Self-Supervised Learning-based MOS Prediction NetworksCode0
A Tale of Two Models: Constructing Evasive Attacks on Edge ModelsCode0
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach—0
How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct—0
Unconditional Image-Text Pair Generation with Multimodal Cross QuantizerCode0
INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold—0
Composite Code Sparse Autoencoders for first stage retrieval—0
Secure Formation Control via Edge Computing Enabled by Fully Homomorphic Encryption and Mixed Uniform-Logarithmic Quantization—0
Joint Coreset Construction and Quantization for Distributed Machine Learning—0
Non-asymptotic spectral bounds on the -entropy of kernel classes—0
Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method—0
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization—0
Characterizing and Understanding the Behavior of Quantized Models for Reliable DeploymentCode0
Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis—0
Bimodal Distributed Binarized Neural NetworksCode0
Soft Threshold Ternary 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