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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1326–1350 of 1915 papers

TitleStatusHype
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models—0
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation—0
VAENAS: Sampling Matters in Neural Architecture Search—0
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation—0
VINNAS: Variational Inference-based Neural Network Architecture Search—0
Visionary: Vision architecture discovery for robot learning—0
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation—0
Warm-starting DARTS using meta-learning—0
WAS-VTON: Warping Architecture Search for Virtual Try-on Network—0
Weak NAS Predictor Is All You Need—0
Weight-Entanglement Meets Gradient-Based Neural Architecture Search—0
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap—0
WeNet: Weighted Networks for Recurrent Network Architecture Search—0
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning—0
What to expect of hardware metric predictors in NAS—0
When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor—0
XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks—0
XferNAS: Transfer Neural Architecture Search—0
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars—0
Yoga Pose Classification Using Transfer Learning—0
ZARTS: On Zero-order Optimization for Neural Architecture Search—0
ZenDet: Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search—0
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition—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-RDARTS-L2Accuracy (Test)46.71—Unverified
4β-SDARTS-RSAccuracy (Test)46.71—Unverified
5ASE-NAS+Accuracy (Val)46.66—Unverified
6NARAccuracy (Test)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
7NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
8DARTS (first order)Top-1 Error Rate3—Unverified
9NASGEPTop-1 Error Rate2.82—Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified