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

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Showing 951–1000 of 1915 papers

TitleStatusHype
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural NetworksCode1
SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker VerificationCode1
Self-Supervised Neural Architecture Search for Imbalanced DatasetsCode0
Forecasting of COVID-19 Cases, Using an Evolutionary Neural Architecture Search Approach—0
Neural Architecture Search in operational context: a remote sensing case-study—0
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking—0
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search—0
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography—0
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search—0
Domain Adaptation by Maximizing Population Correlation with Neural Architecture Search—0
RobustART: Benchmarking Robustness on Architecture Design and Training TechniquesCode1
ReconfigISP: Reconfigurable Camera Image Processing PipelineCode1
RepNAS: Searching for Efficient Re-parameterizing BlocksCode1
Neural Ensemble Search via Bayesian Sampling—0
Automated Robustness with Adversarial Training as a Post-Processing Step—0
NAS-OoD: Neural Architecture Search for Out-of-Distribution GeneralizationCode1
ISyNet: Convolutional Neural Networks design for AI acceleratorCode0
Edge-featured Graph Neural Architecture Search—0
NASI: Label- and Data-agnostic Neural Architecture Search at Initialization—0
Searching for Efficient Multi-Stage Vision TransformersCode1
Searching for Two-Stream Models in Multivariate Space for Video Recognition—0
Analyzing and Mitigating Interference in Neural Architecture Search—0
StressNAS: Affect State and Stress Detection Using Neural Architecture Search—0
Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded MetricsCode1
Lightweight Monocular Depth with a Novel Neural Architecture Search Method—0
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge—0
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization—0
Pooling Architecture Search for Graph ClassificationCode1
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge DistillationCode1
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer—0
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency ShiftCode1
D-DARTS: Distributed Differentiable Architecture SearchCode0
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS—0
Trends in Neural Architecture Search: Towards the Acceleration of Search—0
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
Single-DARTS: Towards Stable Architecture SearchCode0
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving—0
BN-NAS: Neural Architecture Search with Batch NormalizationCode1
Probeable DARTS with Application to Computational PathologyCode0
Evolutionary Algorithms in Approximate Computing: A Survey—0
CONet: Channel Optimization for Convolutional Neural NetworksCode0
FOX-NAS: Fast, On-device and Explainable Neural Architecture SearchCode1
EEEA-Net: An Early Exit Evolutionary Neural Architecture SearchCode1
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive TrainingCode0
Is Differentiable Architecture Search truly a One-Shot Method?—0
Cascade Bagging for Accuracy Prediction with Few Training SamplesCode0
Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity EvaluationCode1
Rethinking Architecture Selection in Differentiable NASCode1
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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