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 15511575 of 1915 papers

TitleStatusHype
An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images0
An Introduction to Neural Architecture Search for Convolutional Networks0
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy0
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search0
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproachCode0
A Semi-Supervised Assessor of Neural Architectures0
A New Deep Neural Architecture Search Pipeline for Face Recognition0
Learning Architectures from an Extended Search Space for Language Modeling0
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions0
Once for All: Train One Network and Specialize it for Efficient Deployment0
How to 0wn the NAS in Your Spare TimeCode0
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks0
MobileDets: Searching for Object Detection Architectures for Mobile AcceleratorsCode0
AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching0
Stage-Wise Neural Architecture SearchCode0
Superkernel Neural Architecture Search for Image Denoising0
When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks0
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search0
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks0
ModuleNet: Knowledge-inherited Neural Architecture Search0
A Neural Architecture Search based Framework for Liquid State Machine Design0
Feature Pyramid GridsCode0
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS0
Benchmarking Deep Spiking Neural Networks on Neuromorphic HardwareCode0
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation0
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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-SDARTS-RSAccuracy (Test)46.71Unverified
4β-RDARTS-L2Accuracy (Test)46.71Unverified
5NARAccuracy (Test)46.66Unverified
6ASE-NAS+Accuracy (Val)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
7DARTS (first order)Top-1 Error Rate3Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified
10NASGEPTop-1 Error Rate2.82Unverified