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 1701–1750 of 1915 papers

TitleStatusHype
Neural Architecture Search by Estimation of Network Structure Distributions—0
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of HyperparametersCode0
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture SearchCode0
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural NetworksCode0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
SqueezeNAS: Fast neural architecture search for faster semantic segmentationCode0
MoGA: Searching Beyond MobileNetV3Code0
AutoML: A Survey of the State-of-the-ArtCode1
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks—0
MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning—0
Efficient Novelty-Driven Neural Architecture Search—0
XferNAS: Transfer Neural Architecture Search—0
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera—0
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures—0
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture SearchCode0
Neural Epitome Search for Architecture-Agnostic Network Compression—0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
EPNAS: Efficient Progressive Neural Architecture Search—0
Hardware/Software Co-Exploration of Neural ArchitecturesCode0
Genetic Network Architecture SearchCode0
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture SearchCode0
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter OptimizationCode0
Evolving Robust Neural Architectures to Defend from Adversarial AttacksCode0
AGAN: Towards Automated Design of Generative Adversarial Networks—0
Densely Connected Search Space for More Flexible Neural Architecture SearchCode0
Posterior-Guided Neural Architecture SearchCode0
Clustering and Classification Networks—0
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural ArchitecturesCode0
XNAS: Neural Architecture Search with Expert AdviceCode1
Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents—0
A Study of the Learning Progress in Neural Architecture Search Techniques—0
Hardware Aware Neural Network Architectures using FbNetCode0
Sample-Efficient Neural Architecture Search by Learning Action Space—0
Scalable Neural Architecture Search for 3D Medical Image Segmentation—0
Continual and Multi-Task Architecture SearchCode0
NAS-FCOS: Fast Neural Architecture Search for Object DetectionCode1
DiCENet: Dimension-wise Convolutions for Efficient NetworksCode0
AutoGrow: Automatic Layer Growing in Deep Convolutional NetworksCode0
One-Shot Neural Architecture Search via Compressive SensingCode0
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation—0
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks—0
Butterfly Transform: An Efficient FFT Based Neural Architecture DesignCode0
Discovering Neural WiringsCode1
RENAS: Reinforced Evolutionary Neural Architecture Search—0
MFAS: Multimodal Fusion Architecture SearchCode0
Efficient Forward Architecture SearchCode1
Efficient Neural Architecture Search via Proximal IterationsCode0
On Network Design Spaces for Visual RecognitionCode1
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution PruningCode0
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers—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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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
7DARTS (first order)Top-1 Error Rate3—Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified
10NASGEPTop-1 Error Rate2.82—Unverified