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 3151–3175 of 4925 papers

TitleStatusHype
TwinDNN: A Tale of Two Deep Neural Networks—0
Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization—0
Two-Bit RIS-Aided Communications at 3.5GHz: Some Insights from the Measurement Results Under Multiple Practical Scenes—0
Two Dimensional Array Imaging with Beam Steered Data—0
Two Heads are Better Than One: Neural Networks Quantization with 2D Hilbert Curve-based Output Representation—0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
Two-layer Near-lossless HDR Coding with Backward Compatibility to JPEG—0
Two-Stage Hashing for Fast Document Retrieval—0
Two-stage iterative Procrustes match algorithm and its application for VQ-based speaker verification—0
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO—0
UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model—0
UDC: Unified DNAS for Compressible TinyML Models—0
ULMRec: User-centric Large Language Model for Sequential Recommendation—0
Ultra-Lightweight Speech Separation via Group Communication—0
Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator—0
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA—0
Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors—0
Ultra-Low Precision 4-bit Training of Deep Neural Networks—0
Ultra-low Precision Multiplication-free Training for Deep Neural Networks—0
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs—0
Uncertainty-Aware Deep Video Compression with Ensembles—0
Uncertainty Estimation in Multi-Agent Distributed Learning—0
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor—0
Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD—0
Understanding Flatness in Generative Models: Its Role and Benefits—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