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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1801–1850 of 1915 papers

TitleStatusHype
Neural Architecture Search for Deep Face Recognition—0
Language Models with TransformersCode0
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object DetectionCode0
ASAP: Architecture Search, Anneal and Prune—0
WeNet: Weighted Networks for Recurrent Network Architecture Search—0
Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?Code0
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 HoursCode0
Branched Multi-Task Networks: Deciding What Layers To Share—0
Architecture Search of Dynamic Cells for Semantic Video Segmentation—0
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage DatasetCode0
Exploring Randomly Wired Neural Networks for Image RecognitionCode0
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting—0
Understanding Neural Architecture Search Techniques—0
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree SearchCode0
DetNAS: Backbone Search for Object DetectionCode0
Deep Demosaicing for Edge Implementation—0
sharpDARTS: Faster and More Accurate Differentiable Architecture SearchCode0
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-IdentificationCode0
MFAS: Multimodal Fusion Architecture Search—0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture SearchCode0
Inductive Transfer for Neural Architecture Optimization—0
Learning Implicitly Recurrent CNNs Through Parameter SharingCode0
Overcoming Multi-Model Forgetting—0
Evaluating the Search Phase of Neural Architecture SearchCode0
Random Search and Reproducibility for Neural Architecture SearchCode0
Fast Task-Aware Architecture Inference—0
Probabilistic Neural Architecture Search—0
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary—0
DVOLVER: Efficient Pareto-Optimal Neural Network Architecture SearchCode0
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity—0
Learnable Embedding Space for Efficient Neural Architecture CompressionCode0
Combinatorial Bayesian Optimization using the Graph Cartesian ProductCode0
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search—0
The Evolved TransformerCode0
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture SearchCode0
CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive AutoencodersCode0
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture SearchCode0
Bayesian Learning of Neural Network ArchitecturesCode0
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image SegmentationCode0
Multi-Objective Reinforced Evolution in Mobile Neural Architecture SearchCode0
Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search—0
Katib: A Distributed General AutoML Platform on Kubernetes—0
Neural Architecture Search Over a Graph Search Space—0
Meta Architecture SearchCode0
SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks—0
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search—0
Evolutionary Neural Architecture Search for Image Restoration—0
IRLAS: Inverse Reinforcement Learning for Architecture SearchCode0
ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search—0
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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