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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1551–1600 of 1915 papers

TitleStatusHype
An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images—0
An Introduction to Neural Architecture Search for Convolutional Networks—0
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy—0
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search—0
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproachCode0
A Semi-Supervised Assessor of Neural Architectures—0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
Learning Architectures from an Extended Search Space for Language Modeling—0
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions—0
Once for All: Train One Network and Specialize it for Efficient Deployment—0
How to 0wn the NAS in Your Spare TimeCode0
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks—0
MobileDets: Searching for Object Detection Architectures for Mobile AcceleratorsCode0
AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching—0
Stage-Wise Neural Architecture SearchCode0
Superkernel Neural Architecture Search for Image Denoising—0
When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks—0
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search—0
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks—0
ModuleNet: Knowledge-inherited Neural Architecture Search—0
A Neural Architecture Search based Framework for Liquid State Machine Design—0
Feature Pyramid GridsCode0
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS—0
Benchmarking Deep Spiking Neural Networks on Neuromorphic HardwareCode0
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation—0
Disturbance-immune Weight Sharing for Neural Architecture Search—0
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search—0
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation—0
ASFD: Automatic and Scalable Face Detector—0
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet—0
BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels—0
FTT-NAS: Discovering Fault-Tolerant Convolutional Neural ArchitectureCode0
Lifelong Learning with Searchable Extension UnitsCode0
PONAS: Progressive One-shot Neural Architecture Search for Very Efficient DeploymentCode0
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS—0
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search—0
Machine Learning based Anomaly Detection for 5G Networks—0
Accelerator-aware Neural Network Design using AutoML—0
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices—0
Real-time Federated Evolutionary Neural Architecture Search—0
ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search—0
BATS: Binary ArchitecTure SearchCode0
BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation—0
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning—0
NAS-Count: Counting-by-Density with Neural Architecture Search—0
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network ArchitecturesCode0
Neural Inheritance Relation Guided One-Shot Layer Assignment Search—0
Neural Architecture Search For Fault Diagnosis—0
How to 0wn NAS in Your Spare TimeCode0
Federated 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-RDARTS-L2Accuracy (Test)46.71—Unverified
4β-SDARTS-RSAccuracy (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
7NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
8DARTS (first order)Top-1 Error Rate3—Unverified
9NASGEPTop-1 Error Rate2.82—Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified