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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 351–375 of 1915 papers

Title	Date	Tasks	Status	Hype
MixPath: A Unified Approach for One-shot Neural Architecture Search	Jan 16, 2020	AutoMLNeural Architecture Search	CodeCode Available	1
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search	Jan 2, 2020	Data AugmentationDiagnostic	CodeCode Available	1
AtomNAS: Fine-Grained End-to-End Neural Architecture Search	Dec 20, 2019	Neural Architecture Search	CodeCode Available	1
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification	Dec 3, 2019	ClassificationComputational Efficiency	CodeCode Available	1
Blockwisely Supervised Neural Architecture Search with Knowledge Distillation	Nov 29, 2019	Knowledge DistillationNeural Architecture Search	CodeCode Available	1
Meta-Learning of Neural Architectures for Few-Shot Learning	Nov 25, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	1
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding	Nov 17, 2019	Graph EmbeddingKnowledge Graph Embedding	CodeCode Available	1
Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search	Nov 10, 2019	Neural Architecture Search	CodeCode Available	1
NAT: Neural Architecture Transformer for Accurate and Compact Architectures	Oct 31, 2019	Neural Architecture SearchReinforcement Learning	CodeCode Available	1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search	Oct 25, 2019	Bayesian OptimizationHyperparameter Optimization	CodeCode Available	1
Searching for A Robust Neural Architecture in Four GPU Hours	Oct 10, 2019	GPUNeural Architecture Search	CodeCode Available	1
ReNAS:Relativistic Evaluation of Neural Architecture Search	Sep 30, 2019	Neural Architecture Search	CodeCode Available	1
Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising	Sep 18, 2019	DenoisingGPU	CodeCode Available	1
Once-for-All: Train One Network and Specialize it for Efficient Deployment	Aug 26, 2019	AllAutoML	CodeCode Available	1
AutoML: A Survey of the State-of-the-Art	Aug 2, 2019	AutoMLFeature Engineering	CodeCode Available	1
XNAS: Neural Architecture Search with Expert Advice	Jun 19, 2019	image-classificationImage Classification	CodeCode Available	1
NAS-FCOS: Fast Neural Architecture Search for Object Detection	Jun 11, 2019	DecoderGPU	CodeCode Available	1
Discovering Neural Wirings	Jun 3, 2019	Feature EngineeringNetwork Pruning	CodeCode Available	1
Efficient Forward Architecture Search	May 31, 2019	feature selectionGPU	CodeCode Available	1
On Network Design Spaces for Visual Recognition	May 30, 2019	Neural Architecture Search	CodeCode Available	1
Searching for MobileNetV3	May 6, 2019	ClassificationDecoder	CodeCode Available	1
Single Path One-Shot Neural Architecture Search with Uniform Sampling	Mar 31, 2019	Neural Architecture SearchQuantization	CodeCode Available	1
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers	Mar 27, 2019	Neural Architecture SearchReinforcement Learning	CodeCode Available	1
NAS-Bench-101: Towards Reproducible Neural Architecture Search	Feb 25, 2019	BenchmarkingNeural Architecture Search	CodeCode Available	1
Evolutionary Neural AutoML for Deep Learning	Feb 18, 2019	AutoMLDeep Learning	CodeCode Available	1

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All datasets ImageNet NAS-Bench-201, ImageNet-16-120 CIFAR-10 NAS-Bench-201, CIFAR-100 NAS-Bench-201, CIFAR-10 CIFAR-10 Image Classification CIFAR-100 NATS-Bench Topology, CIFAR-10 NATS-Bench Topology, CIFAR-100 NATS-Bench Topology, ImageNet16-120 Food-101 NAS-Bench-101

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SPOS (ProxylessNAS (GPU) latency)	Accuracy	75.3	—	Unverified
2	SPOS (FBNet-C latency)	Accuracy	75.1	—	Unverified
3	SPOS (block search + channel search)	Accuracy	74.7	—	Unverified
4	MUXNet-xs	Top-1 Error Rate	33.3	—	Unverified
5	FBNetV2-F1	Top-1 Error Rate	31.7	—	Unverified
6	LayerNAS-60M	Top-1 Error Rate	31	—	Unverified
7	NASGEP	Top-1 Error Rate	29.51	—	Unverified
8	MUXNet-s	Top-1 Error Rate	28.4	—	Unverified
9	NN-MASS-A	Top-1 Error Rate	27.1	—	Unverified
10	FBNetV2-F3	Top-1 Error Rate	26.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CR-LSO	Accuracy (Test)	46.98	—	Unverified
2	Shapley-NAS	Accuracy (Test)	46.85	—	Unverified
3	β-RDARTS-L2	Accuracy (Test)	46.71	—	Unverified
4	β-SDARTS-RS	Accuracy (Test)	46.71	—	Unverified
5	ASE-NAS+	Accuracy (Val)	46.66	—	Unverified
6	NAR	Accuracy (Test)	46.66	—	Unverified
7	BaLeNAS-TF	Accuracy (Test)	46.54	—	Unverified
8	AG-Net	Accuracy (Test)	46.42	—	Unverified
9	Local search	Accuracy (Test)	46.38	—	Unverified
10	NASBOT	Accuracy (Test)	46.37	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Balanced Mixture	Accuracy (% )	91.55	—	Unverified
2	GDAS	Top-1 Error Rate	3.4	—	Unverified
3	Bonsai-Net	Top-1 Error Rate	3.35	—	Unverified
4	Net2 (2)	Top-1 Error Rate	3.3	—	Unverified
5	μDARTS	Top-1 Error Rate	3.28	—	Unverified
6	NN-MASS- CIFAR-C	Top-1 Error Rate	3.18	—	Unverified
7	NN-MASS- CIFAR-A	Top-1 Error Rate	3	—	Unverified
8	DARTS (first order)	Top-1 Error Rate	3	—	Unverified
9	NASGEP	Top-1 Error Rate	2.82	—	Unverified
10	AlphaX-1 (cutout NASNet)	Top-1 Error Rate	2.82	—	Unverified