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

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Showing 626–650 of 1915 papers

TitleStatusHype
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
Federated Neural Architecture Search with Model-Agnostic Meta Learning—0
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run—0
A systematic review of challenges and proposed solutions in modeling multimodal data—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation—0
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework—0
Asynchronous Evolution of Deep Neural Network Architectures—0
A Survey on Computationally Efficient Neural Architecture Search—0
BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels—0
BRP-NAS: Prediction-based NAS using GCNs—0
Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers—0
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction—0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures—0
Bringing AI To Edge: From Deep Learning's Perspective—0
A Survey on Optimal Transport for Machine Learning: Theory and Applications—0
ActNAS : Generating Efficient YOLO Models using Activation NAS—0
A Survey on Neural Architecture Search Based on Reinforcement Learning—0
A Hardware-Aware System for Accelerating Deep Neural Network Optimization—0
Fast Task-Aware Architecture Inference—0
Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks—0
Branched Multi-Task Networks: Deciding What Layers To Share—0
A Survey on Neural Architecture Search—0
Brain development dictates energy constraints on neural architecture search: cross-disciplinary insights on optimization strategies—0
Bractivate: Dendritic Branching in Segmentation Neural Architecture Search—0
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All datasetsImageNetNAS-Bench-201, ImageNet-16-120CIFAR-10NAS-Bench-201, CIFAR-100NAS-Bench-201, CIFAR-10CIFAR-10 Image ClassificationCIFAR-100NATS-Bench Topology, CIFAR-10NATS-Bench Topology, CIFAR-100NATS-Bench Topology, ImageNet16-120Food-101NAS-Bench-101

Benchmark Results

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