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 2676–2700 of 4925 papers

TitleStatusHype
Learning Accurate Performance Predictors for Ultrafast Automated Model CompressionCode0
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs—0
Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval—0
Benchmarking the Robustness of Quantized Models—0
Unsupervised Speech Representation Pooling Using Vector QuantizationCode0
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks—0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination—0
Blockwise Compression of Transformer-based Models without Retraining—0
A Unified Compression Framework for Efficient Speech-Driven Talking-Face Generation—0
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence—0
FP8 versus INT8 for efficient deep learning inference—0
A Joint Model and Data Driven Method for Distributed Estimation—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTACode0
Tetra-AML: Automatic Machine Learning via Tensor Networks—0
Low-Dose CT Image Reconstruction using Vector Quantized Convolutional Autoencoder with Perceptual Loss—0
Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis—0
An Evaluation of Memory Optimization Methods for Training Neural Networks—0
LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression—0
Towards Accurate Post-Training Quantization for Vision Transformer—0
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance—0
The Quantization Model of Neural ScalingCode0
Scaled Quantization for the Vision Transformer—0
Posthoc Interpretation via Quantization—0
Q-HyViT: Post-Training Quantization of Hybrid Vision Transformers with Bridge Block Reconstruction for IoT SystemsCode0
Show:102550
← PrevPage 108 of 197Next →
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