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

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Showing 1251–1300 of 1915 papers

TitleStatusHype
EfficientPose: Efficient Human Pose Estimation with Neural Architecture SearchCode1
Efficient Incorporation of Multiple Latency Targets in the Once-For-All NetworkCode0
Differential Evolution for Neural Architecture SearchCode1
AdvantageNAS: Efficient Neural Architecture Search with Credit AssignmentCode0
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition—0
Skillearn: Machine Learning Inspired by Humans' Learning Skills—0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Batch Group Normalization—0
Automatic Routability Predictor Development Using Neural Architecture Search—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
CLEARER: Multi-Scale Neural Architecture Search for Image RestorationCode1
Understanding and Exploring the Network with Stochastic ArchitecturesCode0
Differentiable Neural Architecture Search in Equivalent Space with Exploration EnhancementCode0
Revisiting Parameter Sharing for Automatic Neural Channel Number SearchCode1
Adapting Neural Architectures Between DomainsCode0
Optimizing the Neural Architecture of Reinforcement Learning AgentsCode0
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition—0
Inter-layer Transition in Neural Architecture SearchCode0
Learning by Passing Tests, with Application to Neural Architecture Search—0
Multi-objective Neural Architecture Search with Almost No Training—0
aw_nas: A Modularized and Extensible NAS framework—0
Bringing AI To Edge: From Deep Learning's Perspective—0
A Review of Recent Advances of Binary Neural Networks for Edge Computing—0
Efficient Sampling for Predictor-Based Neural Architecture Search—0
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation—0
Evolving Search Space for Neural Architecture SearchCode1
FP-NAS: Fast Probabilistic Neural Architecture Search—0
Continuous Ant-Based Neural Topology Search—0
BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures—0
Large Scale Neural Architecture Search with Polyharmonic Splines—0
Effective, Efficient and Robust Neural Architecture Search—0
Stretchable Cells Help DARTS Search Better—0
AttentiveNAS: Improving Neural Architecture Search via Attentive SamplingCode1
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight TransferCode1
Reducing Inference Latency with Concurrent Architectures for Image Recognition—0
Towards NNGP-guided Neural Architecture SearchCode0
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework—0
Adaptive Linear Span Network for Object Skeleton DetectionCode1
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator SearchCode0
Channel Planting for Deep Neural Networks using Knowledge Distillation—0
VEGA: Towards an End-to-End Configurable AutoML PipelineCode1
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing—0
PV-NAS: Practical Neural Architecture Search for Video Recognition—0
FENAS: Flexible and Expressive Neural Architecture Search—0
Neural Network Design: Learning from Neural Architecture SearchCode0
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
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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