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 2601–2625 of 4925 papers

TitleStatusHype
Regularization-based Framework for Quantization-, Fault- and Variability-Aware Training—0
Regularization in Relevance Learning Vector Quantization Using l one Norms—0
Regularized Residual Quantization: a multi-layer sparse dictionary learning approach—0
Regularized Vector Quantization for Tokenized Image Synthesis—0
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics—0
Reinforcement Learning for Finite Space Mean-Field Type Games—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis—0
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks—0
Reliability-Aware Quantization for Anti-Aging NPUs—0
Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort—0
Reliable edge machine learning hardware for scientific applications—0
ReLU and Addition-based Gated RNN—0
ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog—0
Remember and Recall: Associative-Memory-based Trajectory Prediction—0
RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures—0
RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization—0
Representation Collapsing Problems in Vector Quantization—0
Representation Learning using Event-based STDP—0
Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain—0
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs—0
Rescuing Deep Hashing from Dead Bits Problem—0
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals—0
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems—0
Resiliency of Deep Neural Networks under Quantization—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