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 1426–1450 of 1915 papers

TitleStatusHype
Stretchable Cells Help DARTS Search Better—0
Reducing Inference Latency with Concurrent Architectures for Image Recognition—0
Towards NNGP-guided Neural Architecture SearchCode0
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
Channel Planting for Deep Neural Networks using Knowledge Distillation—0
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator SearchCode0
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing—0
PV-NAS: Practical Neural Architecture Search for Video Recognition—0
Neural Network Design: Learning from Neural Architecture SearchCode0
FENAS: Flexible and Expressive Neural Architecture Search—0
Self-supervised Representation Learning for Evolutionary Neural Architecture SearchCode0
Resource-Aware Pareto-Optimal Automated Machine Learning Platform—0
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data—0
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture SearchCode0
Task-Aware Neural Architecture SearchCode0
Neural Architecture Performance Prediction Using Graph Neural Networks—0
ABC-Di: Approximate Bayesian Computation for Discrete DataCode0
How Does Supernet Help in Neural Architecture Search?—0
AutoADR: Automatic Model Design for Ad Relevance—0
Direct Federated Neural Architecture Search—0
Revisiting Neural Architecture Search—0
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework—0
Multi-path Neural Networks for On-device Multi-domain Visual Classification—0
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks—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