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 3001–3025 of 4925 papers

TitleStatusHype
Tetra-AML: Automatic Machine Learning via Tensor Networks—0
Di^2Pose: Discrete Diffusion Model for Occluded 3D Human Pose Estimation—0
Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR—0
DoStoVoQ: Doubly Stochastic Voronoi Vector Quantization SGD for Federated Learning—0
Texture CNN for Thermoelectric Metal Pipe Image Classification—0
The Bach Doodle: Approachable music composition with machine learning at scale—0
The Binary and Ternary Quantization Can Improve Feature Discrimination—0
The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention—0
The bottleneck and ceiling effects in quantized tracking control of heterogeneous multi-agent systems under DoS attacks—0
The Bussgang Decomposition of Non-Linear Systems: Basic Theory and MIMO Extensions—0
The Canonical Distortion Measure for Vector Quantization and Function Approximation—0
The Convergence of Sparsified Gradient Methods—0
The Cramer-Rao Bound for Signal Parameter Estimation from Quantized Data—0
The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning—0
The effect of fatigue on the performance of online writer recognition—0
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective—0
The Hidden Cost of an Image: Quantifying the Energy Consumption of AI Image Generation—0
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models—0
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs—0
The Impact of Quantization on the Robustness of Transformer-based Text Classifiers—0
The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited—0
The Nature of Mathematical Modeling and Probabilistic Optimization Engineering in Generative AI—0
The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures—0
The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns—0
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study—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