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 2076–2100 of 4925 papers

TitleStatusHype
Distributed Learning with Compressed Gradient Differences—0
Distributed Energy Resource Management: All-Time Resource-Demand Feasibility, Delay-Tolerance, Nonlinearity, and Beyond—0
Binarized Neural Network for Single Image Super Resolution—0
An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks—0
A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM—0
Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks—0
An Application of Backpropagation Artificial Neural Network Method for Measuring The Severity of Osteoarthritis—0
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation—0
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation—0
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing—0
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Distributed CPU Scheduling Subject to Nonlinear Constraints—0
Distributed Convolutional Neural Network Training on Mobile and Edge Clusters—0
Distributed Constraint-Coupled Optimization over Lossy Networks—0
Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication—0
MARRS: Masked Autoregressive Unit-based Reaction Synthesis—0
Image De-Quantization Using Generative Models as Priors—0
Distributed Chernoff Test: Optimal decision systems over networks—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Splitting—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation—0
Distortion-Controlled Dithering with Reduced Recompression Rate—0
An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM—0
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics—0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
Bilinear Random Projections for Locality-Sensitive Binary Codes—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