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 1301–1350 of 1915 papers

TitleStatusHype
Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition—0
NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization—0
Differentiable Network Adaption with Elastic Search Space—0
A resource-efficient method for repeated HPO and NAS problems—0
DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image SegmentationCode0
Visionary: Vision architecture discovery for robot learning—0
Recovering Quantitative Models of Human Information Processing with Differentiable Architecture SearchCode0
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising—0
Enhanced Gradient for Differentiable Architecture Search—0
Fisher Task Distance and Its Application in Neural Architecture SearchCode0
AutoSpace: Neural Architecture Search with Less Human InterferenceCode0
GPNAS: A Neural Network Architecture Search Framework Based on Graphical Predictor—0
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition—0
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks—0
Pretraining Neural Architecture Search Controllers with Locality-based Self-Supervised LearningCode0
Interleaving Learning, with Application to Neural Architecture Search—0
Neural Architecture Search based on Cartesian Genetic Programming Coding Method—0
Learning by Teaching, with Application to Neural Architecture Search—0
HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search—0
Trainless Model Performance Estimation for Neural Architecture Search—0
Efficient Model Performance Estimation via Feature Histories—0
Auto-tuning of Deep Neural Networks by Conflicting Layer RemovalCode0
Deep reinforcement learning in medical imaging: A literature review—0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
A-DeepPixBis: Attentional Angular Margin for Face Anti-Spoofing—0
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search—0
Improved Automated Machine Learning from Transfer LearningCode0
A Novel Framework for Neural Architecture Search in the Hill Climbing Domain—0
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 Accelerators—0
Dataset Condensation with Differentiable Siamese AugmentationCode0
Multi-Objective Meta Learning—0
Searching for Fast Model Families on Datacenter Accelerators—0
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records—0
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition—0
AACP: Model Compression by Accurate and Automatic Channel Pruning—0
Evolutionary Neural Architecture Search Supporting Approximate Multipliers—0
Self-supervised Cross-silo Federated Neural Architecture Search—0
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search—0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—0
Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT ClassificationCode0
A Comprehensive Survey on Hardware-Aware Neural Architecture Search—0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution—0
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search—0
DICE: Deep Significance Clustering for Outcome-Aware Stratification—0
Generalized Latency Performance Estimation for Once-For-All Neural Architecture SearchCode0
Topology-aware Tensor Decomposition for Meta-graph Learning—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