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 1801–1825 of 1915 papers

Title	Date	Tasks	Status
Neural Architecture Search for Deep Face Recognition	Apr 21, 2019	AutoMLFace Recognition	—Unverified
Language Models with Transformers	Apr 20, 2019	Computational EfficiencyLanguage Modeling	CodeCode Available
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection	Apr 16, 2019	Neural Architecture SearchObject	CodeCode Available
ASAP: Architecture Search, Anneal and Prune	Apr 8, 2019	GPUNeural Architecture Search	—Unverified
WeNet: Weighted Networks for Recurrent Network Architecture Search	Apr 8, 2019	General Classificationimage-classification	—Unverified
Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?	Apr 8, 2019	Neural Architecture SearchReinforcement Learning	CodeCode Available
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours	Apr 5, 2019	General ClassificationGPU	CodeCode Available
Branched Multi-Task Networks: Deciding What Layers To Share	Apr 5, 2019	Multi-Task LearningNeural Architecture Search	—Unverified
Architecture Search of Dynamic Cells for Semantic Video Segmentation	Apr 4, 2019	GPUNeural Architecture Search	—Unverified
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset	Apr 2, 2019	General ClassificationMeta-Learning	CodeCode Available
Exploring Randomly Wired Neural Networks for Image Recognition	Apr 2, 2019	Image ClassificationNeural Architecture Search	CodeCode Available
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting	Mar 31, 2019	Continual LearningNeural Architecture Search	—Unverified
Understanding Neural Architecture Search Techniques	Mar 31, 2019	Computational EfficiencyGraph Similarity	—Unverified
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search	Mar 26, 2019	GPUImage Captioning	CodeCode Available
DetNAS: Backbone Search for Object Detection	Mar 26, 2019	General Classificationimage-classification	CodeCode Available
Deep Demosaicing for Edge Implementation	Mar 26, 2019	DemosaickingNeural Architecture Search	—Unverified
sharpDARTS: Faster and More Accurate Differentiable Architecture Search	Mar 23, 2019	Hyperparameter OptimizationImage Classification	CodeCode Available
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification	Mar 23, 2019	ClassificationGeneral Classification	CodeCode Available
MFAS: Multimodal Fusion Architecture Search	Mar 15, 2019	Action RecognitionGeneral Classification	—Unverified
Improving Neural Architecture Search Image Classifiers via Ensemble Learning	Mar 14, 2019	Ensemble LearningImage Classification	CodeCode Available
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search	Mar 9, 2019	DecoderGPU	CodeCode Available
Inductive Transfer for Neural Architecture Optimization	Mar 8, 2019	GPUimage-classification	—Unverified
Learning Implicitly Recurrent CNNs Through Parameter Sharing	Feb 26, 2019	image-classificationImage Classification	CodeCode Available
Overcoming Multi-Model Forgetting	Feb 21, 2019	modelNeural Architecture Search	—Unverified
Evaluating the Search Phase of Neural Architecture Search	Feb 21, 2019	Neural Architecture Search	CodeCode Available

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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-SDARTS-RS	Accuracy (Test)	46.71	—	Unverified
4	β-RDARTS-L2	Accuracy (Test)	46.71	—	Unverified
5	NAR	Accuracy (Test)	46.66	—	Unverified
6	ASE-NAS+	Accuracy (Val)	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	DARTS (first order)	Top-1 Error Rate	3	—	Unverified
8	NN-MASS- CIFAR-A	Top-1 Error Rate	3	—	Unverified
9	AlphaX-1 (cutout NASNet)	Top-1 Error Rate	2.82	—	Unverified
10	NASGEP	Top-1 Error Rate	2.82	—	Unverified