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 251275 of 1915 papers

TitleStatusHype
ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile DevicesCode1
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired PerspectiveCode1
AutoSNN: Towards Energy-Efficient Spiking Neural NetworksCode1
Neural Architecture Search via Bregman IterationsCode1
einspace: Searching for Neural Architectures from Fundamental OperationsCode1
Searching a Compact Architecture for Robust Multi-Exposure Image FusionCode1
EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture SearchCode1
EfficientPose: Efficient Human Pose Estimation with Neural Architecture SearchCode1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
EfficientTDNN: Efficient Architecture Search for Speaker RecognitionCode1
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion RecognitionCode1
EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture SearchCode1
AOWS: Adaptive and optimal network width search with latency constraintsCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
b-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
β-DARTS: Beta-Decay Regularization for Differentiable Architecture SearchCode1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter OptimizationCode1
Adjoined Networks: A Training Paradigm with Applications to Network CompressionCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel DimensionsCode1
FEAR: A Simple Lightweight Method to Rank ArchitecturesCode1
AutoSpeech: Neural Architecture Search for Speaker RecognitionCode1
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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
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)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