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 1451–1500 of 1915 papers

TitleStatusHype
AutoHAS: Efficient Hyperparameter and Architecture Search—0
UFO-BLO: Unbiased First-Order Bilevel Optimization—0
Learning to Rank Learning Curves—0
FBNetV3: Joint Architecture-Recipe Search using Predictor PretrainingCode0
MemNAS: Memory-Efficient Neural Architecture Search With Grow-Trim LearningCode0
SP-NAS: Serial-to-Parallel Backbone Search for Object DetectionCode0
GP-NAS: Gaussian Process Based Neural Architecture Search—0
Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity MaximizationCode1
All in One Bad Weather Removal Using Architectural Search—0
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions—0
Block-Wisely Supervised Neural Architecture Search With Knowledge DistillationCode1
Hyperparameter optimization with REINFORCE and Transformers—0
DC-NAS: Divide-and-Conquer Neural Architecture Search—0
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass LensCode0
HAT: Hardware-Aware Transformers for Efficient Natural Language ProcessingCode1
An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images—0
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture SearchCode1
Evolutionary NAS with Gene Expression Programming of Cellular EncodingCode0
An Introduction to Neural Architecture Search for Convolutional Networks—0
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy—0
AOWS: Adaptive and optimal network width search with latency constraintsCode1
Rethinking Performance Estimation in Neural Architecture SearchCode1
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search—0
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproachCode0
A Semi-Supervised Assessor of Neural Architectures—0
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance ImagingCode1
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
Neural Architecture TransferCode1
AutoSpeech: Neural Architecture Search for Speaker RecognitionCode1
Noisy Differentiable Architecture SearchCode1
Learning Architectures from an Extended Search Space for Language Modeling—0
Exploring the Loss Landscape in Neural Architecture SearchCode1
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions—0
Once for All: Train One Network and Specialize it for Efficient Deployment—0
Towards Fast Adaptation of Neural Architectures with Meta LearningCode1
How to 0wn the NAS in Your Spare TimeCode0
MobileDets: Searching for Object Detection Architectures for Mobile AcceleratorsCode0
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks—0
Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces From ImagesCode1
Angle-based Search Space Shrinking for Neural Architecture SearchCode1
AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching—0
Deep Multimodal Neural Architecture SearchCode1
Stage-Wise Neural Architecture SearchCode0
Local Search is a Remarkably Strong Baseline for Neural Architecture SearchCode1
Superkernel Neural Architecture Search for Image Denoising—0
When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks—0
Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture SearchCode1
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search—0
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks—0
Geometry-Aware Gradient Algorithms for Neural Architecture SearchCode1
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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