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 3501–3525 of 4925 papers

TitleStatusHype
An Implementation of Vector Quantization using the Genetic Algorithm Approach—0
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware TransformationCode1
The Wavefunction of Continuous-Time Recurrent Neural Networks—0
Confounding Tradeoffs for Neural Network QuantizationCode1
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure AggregationCode0
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoderCode0
Group Quantization of Quadratic Hamiltonians in Finance—0
BRECQ: Pushing the Limit of Post-Training Quantization by Block ReconstructionCode1
Distribution Adaptive INT8 Quantization for Training CNNs—0
On the Universal Transformation of Data-Driven Models to Control SystemsCode1
Sparsification via Compressed Sensing for Automatic Speech Recognition—0
Enabling Binary Neural Network Training on the EdgeCode1
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference—0
Communication-efficient k-Means for Edge-based Machine Learning—0
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning—0
Refining a -nearest neighbor graph for a computationally efficient spectral clusteringCode0
Symbolic Models for Infinite Networks of Control Systems: A Compositional Approach—0
Compressed Object DetectionCode0
Low Bit-Rate Wideband Speech Coding: A Deep Generative Model based Approach—0
Progressive Neural Image Compression with Nested Quantization and Latent Ordering—0
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded PlatformsCode1
Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering—0
FEDZIP: A Compression Framework for Communication-Efficient Federated LearningCode0
Benchmarking Quantized Neural Networks on FPGAs with FINNCode1
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices—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