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 1726–1750 of 1915 papers

Title	Date	Tasks	Status	Hype
Posterior-Guided Neural Architecture Search	Jun 23, 2019	GPUimage-classification	CodeCode Available	0
Clustering and Classification Networks	Jun 20, 2019	ClassificationClustering	—Unverified	0
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures	Jun 19, 2019	Neural Architecture Search	CodeCode Available	0
XNAS: Neural Architecture Search with Expert Advice	Jun 19, 2019	image-classificationImage Classification	CodeCode Available	1
Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents	Jun 19, 2019	Neural Architecture SearchTransfer Learning	—Unverified	0
A Study of the Learning Progress in Neural Architecture Search Techniques	Jun 18, 2019	Neural Architecture Search	—Unverified	0
Hardware Aware Neural Network Architectures using FbNet	Jun 17, 2019	BenchmarkingNeural Architecture Search	CodeCode Available	0
Sample-Efficient Neural Architecture Search by Learning Action Space	Jun 17, 2019	Evolutionary AlgorithmsNeural Architecture Search	—Unverified	0
Scalable Neural Architecture Search for 3D Medical Image Segmentation	Jun 13, 2019	DecoderImage Segmentation	—Unverified	0
Continual and Multi-Task Architecture Search	Jun 12, 2019	Continual LearningGeneral Classification	CodeCode Available	0
NAS-FCOS: Fast Neural Architecture Search for Object Detection	Jun 11, 2019	DecoderGPU	CodeCode Available	1
DiCENet: Dimension-wise Convolutions for Efficient Networks	Jun 8, 2019	image-classificationImage Classification	CodeCode Available	0
AutoGrow: Automatic Layer Growing in Deep Convolutional Networks	Jun 7, 2019	Neural Architecture Search	CodeCode Available	0
One-Shot Neural Architecture Search via Compressive Sensing	Jun 7, 2019	Compressive SensingMeta-Learning	CodeCode Available	0
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation	Jun 6, 2019	GPUImage Segmentation	—Unverified	0
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks	Jun 6, 2019	image-classificationImage Classification	—Unverified	0
Butterfly Transform: An Efficient FFT Based Neural Architecture Design	Jun 5, 2019	Neural Architecture Search	CodeCode Available	0
Discovering Neural Wirings	Jun 3, 2019	Feature EngineeringNetwork Pruning	CodeCode Available	1
RENAS: Reinforced Evolutionary Neural Architecture Search	Jun 1, 2019	Neural Architecture SearchSemantic Segmentation	—Unverified	0
MFAS: Multimodal Fusion Architecture Search	Jun 1, 2019	Action RecognitionGeneral Classification	CodeCode Available	0
Efficient Forward Architecture Search	May 31, 2019	feature selectionGPU	CodeCode Available	1
Efficient Neural Architecture Search via Proximal Iterations	May 30, 2019	GPUNeural Architecture Search	CodeCode Available	0
On Network Design Spaces for Visual Recognition	May 30, 2019	Neural Architecture Search	CodeCode Available	1
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning	May 28, 2019	AutoMLGPU	CodeCode Available	0
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers	May 28, 2019	BIG-bench Machine LearningNeural Architecture Search	—Unverified	0

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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