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 26–50 of 1915 papers

Title	Date	Tasks	Status	Hype
Nix-TTS: Lightweight and End-to-End Text-to-Speech via Module-wise Distillation	Mar 29, 2022	CPUDecoder	CodeCode Available	2
A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation	Apr 25, 2024	Autonomous DrivingEvolutionary Algorithms	CodeCode Available	2
From Tiny Machine Learning to Tiny Deep Learning: A Survey	Jun 21, 2025	AutoMLModel Optimization	CodeCode Available	2
Learning Efficient Convolutional Networks through Network Slimming	Aug 22, 2017	image-classificationImage Classification	CodeCode Available	2
DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning	Apr 18, 2022	Active LearningContinual Learning	CodeCode Available	2
AMC: AutoML for Model Compression and Acceleration on Mobile Devices	Feb 10, 2018	AutoMLGPU	CodeCode Available	2
ALBench: A Framework for Evaluating Active Learning in Object Detection	Jul 27, 2022	Active Learningimage-classification	CodeCode Available	2
Combinatorial Optimization with Automated Graph Neural Networks	Jun 5, 2024	Combinatorial OptimizationGraph Embedding	CodeCode Available	2
GAN Compression: Efficient Architectures for Interactive Conditional GANs	Mar 19, 2020	Image GenerationNeural Architecture Search	CodeCode Available	2
UL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture Search	Mar 1, 2025	Neural Architecture SearchSpeech Enhancement	CodeCode Available	2
AutoSpeech: Neural Architecture Search for Speaker Recognition	May 7, 2020	image-classificationImage Classification	CodeCode Available	1
AutoSNN: Towards Energy-Efficient Spiking Neural Networks	Jan 30, 2022	Neural Architecture Search	CodeCode Available	1
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization	May 3, 2021	AutoMLHyperparameter Optimization	CodeCode Available	1
AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression	May 25, 2021	Neural Architecture Search	CodeCode Available	1
AFter: Attention-based Fusion Router for RGBT Tracking	May 4, 2024	Neural Architecture SearchRgb-T Tracking	CodeCode Available	1
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search	Oct 25, 2019	Bayesian OptimizationHyperparameter Optimization	CodeCode Available	1
Discovering Neural Wirings	Jun 3, 2019	Feature EngineeringNetwork Pruning	CodeCode Available	1
Accelerating Neural Architecture Search via Proxy Data	Jun 9, 2021	GPUNeural Architecture Search	CodeCode Available	1
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning	Jan 28, 2023	Bayesian OptimisationNeural Architecture Search	CodeCode Available	1
AutoML: A Survey of the State-of-the-Art	Aug 2, 2019	AutoMLFeature Engineering	CodeCode Available	1
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification	Oct 21, 2021	ClassificationHyperspectral Image Classification	CodeCode Available	1
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification	Aug 4, 2023	ClassificationEarly Classification	CodeCode Available	1
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation	Oct 14, 2022	CPUMachine Translation	CodeCode Available	1
Adaptive Linear Span Network for Object Skeleton Detection	Nov 8, 2020	Edge DetectionNeural Architecture Search	CodeCode Available	1
Bayesian Model Selection, the Marginal Likelihood, and Generalization	Feb 23, 2022	modelModel Selection	CodeCode Available	1

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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