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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 1501–1550 of 1915 papers

TitleStatusHype
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel DimensionsCode1
ModuleNet: Knowledge-inherited Neural Architecture Search—0
A Neural Architecture Search based Framework for Liquid State Machine Design—0
Feature Pyramid GridsCode0
Neural Architecture Search for Lightweight Non-Local NetworksCode1
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS—0
Neural Architecture Generator OptimizationCode1
Benchmarking Deep Spiking Neural Networks on Neuromorphic HardwareCode0
MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task LearningCode1
MUXConv: Information Multiplexing in Convolutional Neural NetworksCode1
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation—0
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Disturbance-immune Weight Sharing for Neural Architecture Search—0
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
NPENAS: Neural Predictor Guided Evolution for Neural Architecture SearchCode1
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level ReformulationCode1
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search—0
Hit-Detector: Hierarchical Trinity Architecture Search for Object DetectionCode1
Are Labels Necessary for Neural Architecture Search?Code1
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
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage ModelsCode1
Model-based Asynchronous Hyperparameter and Neural Architecture SearchCode3
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
GAN Compression: Efficient Architectures for Interactive Conditional GANsCode2
AutoSTR: Efficient Backbone Search for Scene Text RecognitionCode1
AutoGluon-Tabular: Robust and Accurate AutoML for Structured DataCode3
PONAS: Progressive One-shot Neural Architecture Search for Very Efficient DeploymentCode0
Hierarchical Neural Architecture Search for Single Image Super-ResolutionCode1
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS—0
Searching Central Difference Convolutional Networks for Face Anti-SpoofingCode1
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
Real-time Federated Evolutionary Neural Architecture Search—0
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices—0
BATS: Binary ArchitecTure SearchCode0
ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search—0
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
Searching for Winograd-aware Quantized NetworksCode1
Semi-Supervised Neural Architecture SearchCode1
Neural Architecture Search for Compressed Sensing Magnetic Resonance Image ReconstructionCode1
DSNAS: Direct Neural Architecture Search without Parameter RetrainingCode1
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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