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 3676–3700 of 4925 papers

TitleStatusHype
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
Robustness-aware 2-bit quantization with real-time performance for neural network—0
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going BeyondCode0
Check-N-Run: A Checkpointing System for Training Deep Learning Recommendation Models—0
CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images—0
Volumetric Calculation of Quantization Error in 3-D Vision Systems—0
Federated Learning in Adversarial Settings—0
FPRaker: A Processing Element For Accelerating Neural Network Training—0
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer—0
An Investigation on Different Underlying Quantization Schemes for Pre-trained Language Models—0
Enhancement Of Coded Speech Using a Mask-Based Post-Filter—0
Real-time Mask Detection on Google Edge TPU—0
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture SearchCode1
3DMolNet: A Generative Network for Molecular Structures—0
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP ModelsCode0
Optimal Gradient Compression for Distributed and Federated Learning—0
Characterising Bias in Compressed Models—0
Joint Pruning & Quantization for Extremely Sparse Neural Networks—0
Sparse Quantized Spectral Clustering—0
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning—0
Compress Polyphone Pronunciation Prediction Model with Shared Labels—0
BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization—0
Faster Binary Embeddings for Preserving Euclidean DistancesCode0
How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text—0
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—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