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 2526–2550 of 4925 papers

TitleStatusHype
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM—0
Communication-Efficient Federated Distillation—0
Communication Efficient Federated Learning over Multiple Access Channels—0
Communication-Efficient Federated Learning via Optimal Client Sampling—0
Communication-Efficient Federated Learning via Quantized Compressed Sensing—0
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks—0
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks—0
Communication-efficient k-Means for Edge-based Machine Learning—0
Communication Efficient SGD via Gradient Sampling With Bayes Prior—0
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Compact and Robust Deep Learning Architecture for Fluorescence Lifetime Imaging and FPGA Implementation—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
Compact Neural Graphics Primitives with Learned Hash Probing—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling—0
Comparison of 14 different families of classification algorithms on 115 binary datasets—0
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other—0
Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners—0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—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