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 13261350 of 1915 papers

TitleStatusHype
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search0
Improved Automated Machine Learning from Transfer LearningCode0
A Novel Framework for Neural Architecture Search in the Hill Climbing Domain0
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferenceCode0
Contrastive Self-supervised Neural Architecture SearchCode0
Rethinking Co-design of Neural Architectures and Hardware Accelerators0
Dataset Condensation with Differentiable Siamese AugmentationCode0
Multi-Objective Meta Learning0
Searching for Fast Model Families on Datacenter Accelerators0
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records0
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition0
AACP: Model Compression by Accurate and Automatic Channel Pruning0
Evolutionary Neural Architecture Search Supporting Approximate Multipliers0
Self-supervised Cross-silo Federated Neural Architecture Search0
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond0
Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT ClassificationCode0
A Comprehensive Survey on Hardware-Aware Neural Architecture Search0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution0
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search0
DICE: Deep Significance Clustering for Outcome-Aware Stratification0
Generalized Latency Performance Estimation for Once-For-All Neural Architecture SearchCode0
Topology-aware Tensor Decomposition for Meta-graph Learning0
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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