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 2426–2450 of 4925 papers

TitleStatusHype
Data Upcycling Knowledge Distillation for Image Super-ResolutionCode0
GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks—0
Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantizationCode0
Causal-DFQ: Causality Guided Data-free Network QuantizationCode0
From Text to Source: Results in Detecting Large Language Model-Generated Content—0
Poster: Self-Supervised Quantization-Aware Knowledge Distillation—0
Activation Compression of Graph Neural Networks using Block-wise Quantization with Improved Variance MinimizationCode0
Benchmarking quantized LLaMa-based models on the Brazilian Secondary School Exam—0
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision—0
Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representations—0
SPFQ: A Stochastic Algorithm and Its Error Analysis for Neural Network Quantization—0
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information—0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition—0
DeepliteRT: Computer Vision at the Edge—0
Deep Learning based Fast and Accurate Beamforming for Millimeter-Wave Systems—0
Semantic Text Compression for Classification—0
RIS-Assisted Energy Harvesting Gains for Bistatic Backscatter Networks: Performance Analysis and RIS Phase Optimization—0
A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge—0
Towards Practical and Efficient Image-to-Speech Captioning with Vision-Language Pre-training and Multi-modal Tokens—0
Communication Efficient Private Federated Learning Using DitheringCode0
Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling—0
Understanding the Impact of Post-Training Quantization on Large Language Models—0
Quantized Fourier and Polynomial Features for more Expressive Tensor Network ModelsCode0
One-Bit-Aided Modulo Sampling for DOA Estimation—0
Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning—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