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 3426–3450 of 4925 papers

TitleStatusHype
NoiseVC: Towards High Quality Zero-Shot Voice Conversion—0
Soft then Hard: Rethinking the Quantization in Neural Image Compression—0
Distributed Learning Systems with First-order MethodsCode1
Q-matrix Unaware Double JPEG Detection using DCT-Domain Deep BiLSTM Network—0
A Novel Unified Model for Multi-exposure Stereo Coding Based on Low Rank Tucker-ALS and 3D-HEVC—0
Functional quantization of rough volatility and applications to volatility derivatives—0
Quantized State Feedback Stabilization of Nonlinear Systems under Denial-of-Service—0
Towards On-Device Face Recognition in Body-worn Cameras—0
Learned transform compression with optimized entropy encodingCode0
TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT—0
Binary Neural Network for Speaker Verification—0
Quantized Gromov-WassersteinCode1
Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD—0
Network Quantization with Element-wise Gradient ScalingCode1
Arabic Compact Language Modelling for Resource Limited Devices—0
Training Multi-bit Quantized and Binarized Networks with A Learnable Symmetric QuantizerCode1
Bit-Mixer: Mixed-precision networks with runtime bit-width selection—0
Integer-only Zero-shot Quantization for Efficient Speech RecognitionCode1
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Zero-shot Adversarial Quantization—0
Automated Backend-Aware Post-Training Quantization—0
Scalable and Efficient Neural Speech Coding: A Hybrid Design—0
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design—0
A Survey of Quantization Methods for Efficient Neural Network Inference—0
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation—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