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

Showing 17011725 of 1915 papers

TitleStatusHype
Neural Architecture Search by Estimation of Network Structure Distributions0
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 Blocks0
MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning0
Efficient Novelty-Driven Neural Architecture Search0
XferNAS: Transfer Neural Architecture Search0
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera0
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures0
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture SearchCode0
Neural Epitome Search for Architecture-Agnostic Network Compression0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search0
EPNAS: Efficient Progressive Neural Architecture Search0
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 Networks0
Densely Connected Search Space for More Flexible Neural Architecture SearchCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-RDARTS-L2Accuracy (Test)46.71Unverified
4β-SDARTS-RSAccuracy (Test)46.71Unverified
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
8DARTS (first order)Top-1 Error Rate3Unverified
9NASGEPTop-1 Error Rate2.82Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified