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 1576–1600 of 1915 papers

Title	Date	Tasks	Status	Hype
Deeper Insights into Weight Sharing in Neural Architecture Search	Jan 6, 2020	Neural Architecture Search	CodeCode Available	0
EcoNAS: Finding Proxies for Economical Neural Architecture Search	Jan 5, 2020	GPUNeural Architecture Search	—Unverified	0
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search	Jan 2, 2020	Data AugmentationDiagnostic	CodeCode Available	1
Neural Architecture Search in a Proxy Validation Loss Landscape	Jan 1, 2020	Neural Architecture Search	—Unverified	0
ISBNet: Instance-aware Selective Branching Networks	Jan 1, 2020	Neural Architecture Search	—Unverified	0
Scalable NAS with Factorizable Architectural Parameters	Dec 31, 2019	image-classificationImage Classification	—Unverified	0
Modeling Neural Architecture Search Methods for Deep Networks	Dec 31, 2019	Neural Architecture Search	—Unverified	0
Searching for Stage-wise Neural Graphs In the Limit	Dec 30, 2019	Neural Architecture Search	—Unverified	0
Neural Architecture Search on Acoustic Scene Classification	Dec 30, 2019	Acoustic Scene ClassificationClassification	—Unverified	0
NAS evaluation is frustratingly hard	Dec 28, 2019	Image ClassificationNeural Architecture Search	CodeCode Available	0
BETANAS: BalancEd TrAining and selective drop for Neural Architecture Search	Dec 24, 2019	Neural Architecture Search	—Unverified	0
Computation Reallocation for Object Detection	Dec 24, 2019	Instance SegmentationNeural Architecture Search	—Unverified	0
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild	Dec 23, 2019	GPUNeural Architecture Search	CodeCode Available	0
TextNAS: A Neural Architecture Search Space tailored for Text Representation	Dec 23, 2019	General ClassificationNatural Language Inference	—Unverified	0
FasterSeg: Searching for Faster Real-time Semantic Segmentation	Dec 23, 2019	Neural Architecture SearchReal-Time Semantic Segmentation	CodeCode Available	0
AtomNAS: Fine-Grained End-to-End Neural Architecture Search	Dec 20, 2019	Neural Architecture Search	CodeCode Available	1
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation	Dec 20, 2019	Image SegmentationMedical Image Segmentation	—Unverified	0
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud	Dec 18, 2019	Neural Architecture SearchPrivacy Preserving	—Unverified	0
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data	Dec 17, 2019	Neural Architecture SearchReinforcement Learning	CodeCode Available	0
UNAS: Differentiable Architecture Search Meets Reinforcement Learning	Dec 16, 2019	Neural Architecture Searchreinforcement-learning	CodeCode Available	0
PreVIous: A Methodology for Prediction of Visual Inference Performance on IoT Devices	Dec 13, 2019	Neural Architecture Search	CodeCode Available	0
Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS	Dec 12, 2019	Hyperparameter OptimizationNeural Architecture Search	—Unverified	0
Leveraging End-to-End Speech Recognition with Neural Architecture Search	Dec 11, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction	Dec 11, 2019	Neural Architecture SearchPrediction	CodeCode Available	0
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization	Dec 10, 2019	Image ClassificationInstance Segmentation	CodeCode Available	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-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