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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 351–400 of 1915 papers

TitleStatusHype
AttentiveNAS: Improving Neural Architecture Search via Attentive SamplingCode1
Efficient Neural Architecture Search via Parameter SharingCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
EfficientPose: Efficient Human Pose Estimation with Neural Architecture SearchCode1
Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity EvaluationCode1
EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture SearchCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight TransferCode1
Searching a Compact Architecture for Robust Multi-Exposure Image FusionCode1
EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture SearchCode1
ChamNet: Towards Efficient Network Design through Platform-Aware Model AdaptationCode1
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion RecognitionCode1
AlphaNet: Improved Training of Supernets with Alpha-DivergenceCode1
PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture SearchCode1
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT DevicesCode1
EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture SearchCode1
Contrastive Neural Architecture Search with Neural Architecture ComparatorsCode1
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree SearchCode1
DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion ModelsCode1
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-IdentificationCode1
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural ReparameterizationCode1
Pruning Self-attentions into Convolutional Layers in Single PathCode1
Hyper-Representations for Pre-Training and Transfer LearningCode1
Rapid Neural Architecture Search by Learning to Generate Graphs from DatasetsCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
ColdNAS: Search to Modulate for User Cold-Start RecommendationCode1
RelativeNAS: Relative Neural Architecture Search via Slow-Fast LearningCode1
Removing Raindrops and Rain Streaks in One GoCode1
Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules ClassificationCode1
Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier SearchCode1
Rethinking Architecture Selection in Differentiable NASCode1
Evolutionary Neural Cascade Search across SupernetworksCode1
Differentiable Architecture Search for Reinforcement LearningCode1
Exploring Relational Context for Multi-Task Dense PredictionCode1
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
Extensible Proxy for Efficient NASCode1
AutoSpace: Neural Architecture Search with Less Human InterferenceCode0
AdvantageNAS: Efficient Neural Architecture Search with Credit AssignmentCode0
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural ArchitectureCode0
A Novel Evolutionary Algorithm for Hierarchical Neural Architecture SearchCode0
AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural NetworksCode0
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-IdentificationCode0
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative GainCode0
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasetsCode0
Heterogeneous Graph Neural Architecture Search with GPT-4Code0
Hardware Aware Neural Network Architectures using FbNetCode0
AutoPose: Searching Multi-Scale Branch Aggregation for Pose EstimationCode0
Fast Hardware-Aware Neural Architecture SearchCode0
Autoequivariant Network Search via Group DecompositionCode0
Seesaw-Net: Convolution Neural Network With Uneven Group ConvolutionCode0
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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