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 4876–4900 of 4925 papers

TitleStatusHype
Additive Quantization for Extreme Vector Compression—0
Locally Linear Hashing for Extracting Non-Linear Manifolds—0
Distance Encoded Product Quantization—0
A Compact and Discriminative Face Track Descriptor—0
Optimized Cartesian K-Means—0
Improving Bilayer Product Quantization for Billion-Scale Approximate Nearest Neighbors in High Dimensions—0
Coding for Random Projections and Approximate Near Neighbor Search—0
Parallel WiSARD object tracker: a ram-based tracking system—0
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval—0
Precision Enhancement of 3D Surfaces from Multiple Compressed Depth Maps—0
Packing and Padding: Coupled Multi-index for Accurate Image Retrieval—0
Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification—0
Stopping Rules for Bag-of-Words Image Search and Its Application in Appearance-Based Localization—0
Codebook based Audio Feature Representation for Music Information Retrieval—0
mARC: Memory by Association and Reinforcement of Contexts—0
Which Space Partitioning Tree to Use for Search?—0
Fast Template Evaluation with Vector Quantization—0
Reduplication across Categories in Cantonese—0
Two Dimensional Array Imaging with Beam Steered Data—0
Regularization in Relevance Learning Vector Quantization Using l one Norms—0
An Application of Backpropagation Artificial Neural Network Method for Measuring The Severity of Osteoarthritis—0
An Efficient Index for Visual Search in Appearance-based SLAM—0
Using Self-Organizing Maps for Sentiment Analysis—0
A Study on Unsupervised Dictionary Learning and Feature Encoding for Action Classification—0
Coding for Random Projections—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