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
Transfer Learning to Learn with Multitask Neural Model Search—0
Transfer Learning with Neural AutoML—0
Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents—0
Transferrable Surrogates in Expressive Neural Architecture Search Spaces—0
Trends in Neural Architecture Search: Towards the Acceleration of Search—0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution—0
Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection—0
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching—0
Tr-NAS: Memory-Efficient Neural Architecture Search with Transferred Blocks—0
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks—0
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification—0
UDC: Unified DNAS for Compressible TinyML Models—0
Multi-trial Neural Architecture Search with Lottery Tickets—0
UFO-BLO: Unbiased First-Order Bilevel Optimization—0
Ultrafast Photorealistic Style Transfer via Neural Architecture Search—0
Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search—0
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning—0
Understanding and Simplifying One-Shot Architecture Search—0
Understanding Neural Architecture Search Techniques—0
Underwater object detection in sonar imagery with detection transformer and Zero-shot neural architecture search—0
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness—0
Uniform-Precision Neural Network Quantization via Neural Channel Expansion—0
UnrealNAS: Can We Search Neural Architectures with Unreal Data?—0
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision—0
Building high accuracy emulators for scientific simulations with deep neural architecture search—0
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models—0
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation—0
VAENAS: Sampling Matters in Neural Architecture Search—0
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation—0
VINNAS: Variational Inference-based Neural Network Architecture Search—0
Visionary: Vision architecture discovery for robot learning—0
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation—0
Warm-starting DARTS using meta-learning—0
WAS-VTON: Warping Architecture Search for Virtual Try-on Network—0
Weak NAS Predictor Is All You Need—0
Weight-Entanglement Meets Gradient-Based Neural Architecture Search—0
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap—0
WeNet: Weighted Networks for Recurrent Network Architecture Search—0
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning—0
What to expect of hardware metric predictors in NAS—0
When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor—0
XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks—0
XferNAS: Transfer Neural Architecture Search—0
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars—0
Yoga Pose Classification Using Transfer Learning—0
ZARTS: On Zero-order Optimization for Neural Architecture Search—0
ZenDet: Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search—0
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition—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-SDARTS-RSAccuracy (Test)46.71—Unverified
4β-RDARTS-L2Accuracy (Test)46.71—Unverified
5NARAccuracy (Test)46.66—Unverified
6ASE-NAS+Accuracy (Val)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