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 1701–1725 of 1915 papers

TitleStatusHype
Efficient Progressive Neural Architecture Search—0
Efficient Sampling for Predictor-Based Neural Architecture Search—0
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation—0
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling—0
Designing deep neural networks for driver intention recognition—0
Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices—0
Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection—0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness—0
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning—0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks—0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey—0
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge—0
Searching for Two-Stream Models in Multivariate Space for Video Recognition—0
Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring—0
Denoising Designs-inherited Search Framework for Image Denoising—0
EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition—0
Delta-NAS: Difference of Architecture Encoding for Predictor-based Evolutionary Neural Architecture Search—0
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition—0
A Lightweight Neural Architecture Search Model for Medical Image Classification—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration—0
Enabling NAS with Automated Super-Network Generation—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
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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