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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 176–200 of 1915 papers

Title	Date	Tasks	Status	Hype
Aligned Structured Sparsity Learning for Efficient Image Super-Resolution	Dec 1, 2021	Image Super-ResolutionKnowledge Distillation	CodeCode Available	1
Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity Evaluation	Aug 10, 2021	GPUNeural Architecture Search	CodeCode Available	1
ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices	Mar 17, 2023	Neural Architecture Search	CodeCode Available	1
EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search	Oct 12, 2022	Neural Architecture Search	CodeCode Available	1
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices	Oct 11, 2023	Neural Architecture Search	CodeCode Available	1
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search	Jul 7, 2020	Neural Architecture Search	CodeCode Available	1
Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles	Jun 14, 2020	Neural Architecture Search	CodeCode Available	1
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search	May 18, 2018	GPUImage Captioning	CodeCode Available	1
Evolutionary Neural Cascade Search across Supernetworks	Mar 8, 2022	Neural Architecture Search	CodeCode Available	1
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight Transfer	Nov 17, 2020	2D Human Pose EstimationKeypoint Detection	CodeCode Available	1
Exploring Relational Context for Multi-Task Dense Prediction	Apr 28, 2021	Neural Architecture SearchPrediction	CodeCode Available	1
Adaptive Cross-Layer Attention for Image Restoration	Mar 4, 2022	DemosaickingDenoising	CodeCode Available	1
AutoML: A Survey of the State-of-the-Art	Aug 2, 2019	AutoMLFeature Engineering	CodeCode Available	1
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration	Dec 1, 2020	DenoisingGPU	CodeCode Available	1
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification	Aug 4, 2023	ClassificationEarly Classification	CodeCode Available	1
β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture Search	Jan 16, 2023	Neural Architecture Search	CodeCode Available	1
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning	Jan 28, 2023	Bayesian OptimisationNeural Architecture Search	CodeCode Available	1
FlowNAS: Neural Architecture Search for Optical Flow Estimation	Jul 4, 2022	image-classificationImage Classification	CodeCode Available	1
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search	Jun 21, 2020	image-classificationImage Classification	CodeCode Available	1
AdvRush: Searching for Adversarially Robust Neural Architectures	Aug 3, 2021	Adversarial RobustnessNeural Architecture Search	CodeCode Available	1
Adjoined Networks: A Training Paradigm with Applications to Network Compression	Jun 10, 2020	Knowledge DistillationNeural Architecture Search	CodeCode Available	1
β-DARTS: Beta-Decay Regularization for Differentiable Architecture Search	Mar 3, 2022	Neural Architecture Search	CodeCode Available	1
AutoGL: A Library for Automated Graph Learning	Apr 11, 2021	AutoMLBIG-bench Machine Learning	CodeCode Available	1
AOWS: Adaptive and optimal network width search with latency constraints	May 21, 2020	Neural Architecture Search	CodeCode Available	1
b-DARTS: Beta-Decay Regularization for Differentiable Architecture Search	Jan 1, 2022	Neural Architecture Search	CodeCode Available	1

Show:10 25 50

← PrevPage 8 of 77Next →

All datasets ImageNet NAS-Bench-201, ImageNet-16-120 CIFAR-10 NAS-Bench-201, CIFAR-100 NAS-Bench-201, CIFAR-10 CIFAR-10 Image Classification CIFAR-100 NATS-Bench Topology, CIFAR-10 NATS-Bench Topology, CIFAR-100 NATS-Bench Topology, ImageNet16-120 Food-101 NAS-Bench-101

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SPOS (ProxylessNAS (GPU) latency)	Accuracy	75.3	—	Unverified
2	SPOS (FBNet-C latency)	Accuracy	75.1	—	Unverified
3	SPOS (block search + channel search)	Accuracy	74.7	—	Unverified
4	MUXNet-xs	Top-1 Error Rate	33.3	—	Unverified
5	FBNetV2-F1	Top-1 Error Rate	31.7	—	Unverified
6	LayerNAS-60M	Top-1 Error Rate	31	—	Unverified
7	NASGEP	Top-1 Error Rate	29.51	—	Unverified
8	MUXNet-s	Top-1 Error Rate	28.4	—	Unverified
9	NN-MASS-A	Top-1 Error Rate	27.1	—	Unverified
10	FBNetV2-F3	Top-1 Error Rate	26.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CR-LSO	Accuracy (Test)	46.98	—	Unverified
2	Shapley-NAS	Accuracy (Test)	46.85	—	Unverified
3	β-SDARTS-RS	Accuracy (Test)	46.71	—	Unverified
4	β-RDARTS-L2	Accuracy (Test)	46.71	—	Unverified
5	NAR	Accuracy (Test)	46.66	—	Unverified
6	ASE-NAS+	Accuracy (Val)	46.66	—	Unverified
7	BaLeNAS-TF	Accuracy (Test)	46.54	—	Unverified
8	AG-Net	Accuracy (Test)	46.42	—	Unverified
9	Local search	Accuracy (Test)	46.38	—	Unverified
10	NASBOT	Accuracy (Test)	46.37	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Balanced Mixture	Accuracy (% )	91.55	—	Unverified
2	GDAS	Top-1 Error Rate	3.4	—	Unverified
3	Bonsai-Net	Top-1 Error Rate	3.35	—	Unverified
4	Net2 (2)	Top-1 Error Rate	3.3	—	Unverified
5	μDARTS	Top-1 Error Rate	3.28	—	Unverified
6	NN-MASS- CIFAR-C	Top-1 Error Rate	3.18	—	Unverified
7	DARTS (first order)	Top-1 Error Rate	3	—	Unverified
8	NN-MASS- CIFAR-A	Top-1 Error Rate	3	—	Unverified
9	AlphaX-1 (cutout NASNet)	Top-1 Error Rate	2.82	—	Unverified
10	NASGEP	Top-1 Error Rate	2.82	—	Unverified