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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
Searching a Compact Architecture for Robust Multi-Exposure Image FusionCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion RecognitionCode1
Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity EvaluationCode1
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT DevicesCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture SearchCode1
Evolutionary Neural AutoML for Deep LearningCode1
Equivalence in Deep Neural Networks via Conjugate Matrix EnsemblesCode1
ChamNet: Towards Efficient Network Design through Platform-Aware Model AdaptationCode1
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge DistillationCode1
Contrastive Neural Architecture Search with Neural Architecture ComparatorsCode1
Evolutionary Neural Architecture Search for Transformer in Knowledge TracingCode1
Integrating Large Circular Kernels into CNNs through Neural Architecture SearchCode1
KNAS: Green Neural Architecture SearchCode1
CLEARER: Multi-Scale Neural Architecture Search for Image RestorationCode1
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree SearchCode1
Rapid Neural Architecture Search by Learning to Generate Graphs from DatasetsCode1
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-IdentificationCode1
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight TransferCode1
Evolving Search Space for Neural Architecture SearchCode1
Exploring Relational Context for Multi-Task Dense PredictionCode1
RelativeNAS: Relative Neural Architecture Search via Slow-Fast LearningCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
ColdNAS: Search to Modulate for User Cold-Start RecommendationCode1
Rethinking Dilated Convolution for Real-time Semantic SegmentationCode1
Rethinking Architecture Selection in Differentiable NASCode1
Multi-conditioned Graph Diffusion for Neural Architecture SearchCode1
Neural Architecture Search for Joint Human Parsing and Pose EstimationCode1
Rethinking Neural Operations for Diverse TasksCode1
Rethinking Performance Estimation in Neural Architecture SearchCode1
Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution HeterogeneityCode1
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture SearchCode1
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
FEAR: A Simple Lightweight Method to Rank ArchitecturesCode1
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
HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanismCode0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
AutoPose: Searching Multi-Scale Branch Aggregation for Pose EstimationCode0
Homogeneous Architecture Augmentation for Neural PredictorCode0
How to 0wn NAS in Your Spare TimeCode0
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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
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
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