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 4376–4400 of 4925 papers

TitleStatusHype
Same, Same But Different - Recovering Neural Network Quantization Error Through Weight FactorizationCode0
Collaborative Quantization for Cross-Modal Similarity Search—0
Supervised Quantization for Similarity Search—0
Deep Triplet QuantizationCode0
Robustness of Generalized Learning Vector Quantization Models against Adversarial AttacksCode0
Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge—0
Model-Based Detector for SSDs in the Presence of Inter-cell Interference—0
Improving Neural Network Quantization without Retraining using Outlier Channel SplittingCode0
Distributed Learning with Compressed Gradient Differences—0
Subspace Robust Wasserstein Distances—0
Learning Space Partitions for Nearest Neighbor SearchCode0
Nonparametric Inference under B-bits Quantization—0
QGAN: Quantized Generative Adversarial Networks—0
Toward Joint Image Generation and Compression using Generative Adversarial Networks—0
On the Uplink Achievable Rate of Massive MIMO System With Low-Resolution ADC and RF Impairments—0
Hybrid coarse-fine classification for head pose estimationCode0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks—0
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison—0
Quantized Epoch-SGD for Communication-Efficient Distributed Learning—0
Mean Estimation from One-Bit Measurements—0
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images—0
DSConv: Efficient Convolution OperatorCode0
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks—0
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-AirCode0
Vector and Line Quantization for Billion-scale Similarity Search on GPUsCode0
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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