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 2951–2975 of 4925 papers

TitleStatusHype
Convergence Rates for Regularized Optimal Transport via Quantization—0
Computing with Hypervectors for Efficient Speaker Identification—0
Ab-initio quantum chemistry with neural-network wavefunctions—0
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation—0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks—0
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates—0
Efficient Adaptive Activation Rounding for Post-Training Quantization—0
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform—0
Are disentangled representations all you need to build speaker anonymization systems?—0
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks—0
G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model—0
What Does a One-Bit Quanta Image Sensor Offer?—0
Towards Practical Single-shot Phase Retrieval with Physics-Driven Deep Neural Network—0
Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis—0
AdaBin: Improving Binary Neural Networks with Adaptive Binary SetsCode0
Learning to Structure an Image with Few Colors and Beyond—0
Design and Analysis of Hardware-limited Non-uniform Task-based Quantizers—0
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering—0
PECAN: A Product-Quantized Content Addressable Memory Network—0
Sampled-data control design for systems with quantized actuators—0
Mixed-Precision Neural Networks: A Survey—0
Quantized Adaptive Subgradient Algorithms and Their Applications—0
Learning Quantization in LDPC Decoders—0
Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization—0
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming—0
Show:102550
← PrevPage 119 of 197Next →
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