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 4801–4825 of 4925 papers

TitleStatusHype
Learning A Deep _ Encoder for Hashing—0
A New Learning Method for Inference Accuracy, Core Occupation, and Performance Co-optimization on TrueNorth Chip—0
Vector Quantization for Machine Vision—0
Scalable Image Retrieval by Sparse Product Quantization—0
Correlation Hashing Network for Efficient Cross-Modal Retrieval—0
2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search—0
Recommender systems inspired by the structure of quantum theory—0
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning—0
Group Invariant Deep Representations for Image Instance Retrieval—0
Quality Adaptive Low-Rank Based JPEG Decoding with Applications—0
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes—0
A Latent-Variable Lattice Model—0
Transformed Residual Quantization for Approximate Nearest Neighbor Search—0
Fixed-Point Performance Analysis of Recurrent Neural Networks—0
An NMF Perspective on Binary Hashing—0
Alternating Co-Quantization for Cross-Modal Hashing—0
Fast Orthogonal Projection Based on Kronecker Product—0
Adaptive Dither Voting for Robust Spatial Verification—0
Web-Scale Image Clustering RevisitedCode0
Hyperpoints and Fine Vocabularies for Large-Scale Location Recognition—0
PQTable: Fast Exact Asymmetric Distance Neighbor Search for Product Quantization Using Hash Tables—0
b-bit Marginal Regression—0
Resiliency of Deep Neural Networks under Quantization—0
Fixed Point Quantization of Deep Convolutional Networks—0
Information Extraction Under Privacy Constraints—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