SOTAVerified

Neural Architecture Search

Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.

Image Credit : NAS with Reinforcement Learning

Papers

Showing 851875 of 1915 papers

TitleStatusHype
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter0
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development0
AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model0
Designing deep neural networks for driver intention recognition0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search0
Self-Programming Artificial Intelligence Using Code-Generating Language Models0
A Design Space Study for LISTA and Beyond0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey0
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge0
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture0
Denoising Designs-inherited Search Framework for Image Denoising0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning0
A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms0
A-DeepPixBis: Attentional Angular Margin for Face Anti-Spoofing0
Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search0
Delta-NAS: Difference of Architecture Encoding for Predictor-based Evolutionary Neural Architecture Search0
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy0
DEGAS: Differentiable Efficient Generator Search0
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition0
A New Deep Neural Architecture Search Pipeline for Face Recognition0
Deep reinforcement learning in medical imaging: A literature review0
AutoML Systems For Medical Imaging0
A Neural Architecture Search Method using Auxiliary Evaluation Metric based on ResNet Architecture0
A Deeper Look at Zero-Cost Proxies for Lightweight NAS0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-RDARTS-L2Accuracy (Test)46.71Unverified
4β-SDARTS-RSAccuracy (Test)46.71Unverified
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
8DARTS (first order)Top-1 Error Rate3Unverified
9NASGEPTop-1 Error Rate2.82Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified