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 3951–3975 of 4925 papers

TitleStatusHype
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
Component Training of Turbo Autoencoders—0
Composite Code Sparse Autoencoders for first stage retrieval—0
Composite Correlation Quantization for Efficient Multimodal Retrieval—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—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