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

Showing 150 of 1915 papers

TitleStatusHype
Structured Pruning for Deep Convolutional Neural Networks: A surveyCode4
DAMO-YOLO : A Report on Real-Time Object Detection DesignCode4
Efficient Automated Deep Learning for Time Series ForecastingCode4
MobileNetV4 -- Universal Models for the Mobile EcosystemCode3
DNA Family: Boosting Weight-Sharing NAS with Block-Wise SupervisionsCode3
Multi-objective Asynchronous Successive HalvingCode3
EfficientNetV2: Smaller Models and Faster TrainingCode3
Model-based Asynchronous Hyperparameter and Neural Architecture SearchCode3
AutoGluon-Tabular: Robust and Accurate AutoML for Structured DataCode3
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksCode3
AnalogNAS-Bench: A NAS Benchmark for Analog In-Memory ComputingCode2
From Tiny Machine Learning to Tiny Deep Learning: A SurveyCode2
UL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture SearchCode2
GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New InsightsCode2
Combinatorial Optimization with Automated Graph Neural NetworksCode2
A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic SegmentationCode2
Evolutionary Computation in the Era of Large Language Model: Survey and RoadmapCode2
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion ModelsCode2
AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary DetectionCode2
ZoomNAS: Searching for Whole-body Human Pose Estimation in the WildCode2
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
Neural Prompt SearchCode2
DeepCore: A Comprehensive Library for Coreset Selection in Deep LearningCode2
Nix-TTS: Lightweight and End-to-End Text-to-Speech via Module-wise DistillationCode2
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language ModelsCode2
Automated Deep Learning: Neural Architecture Search Is Not the EndCode2
Searching Efficient 3D Architectures with Sparse Point-Voxel ConvolutionCode2
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDLCode2
Fine-Grained Stochastic Architecture SearchCode2
GAN Compression: Efficient Architectures for Interactive Conditional GANsCode2
ProxylessNAS: Direct Neural Architecture Search on Target Task and HardwareCode2
AMC: AutoML for Model Compression and Acceleration on Mobile DevicesCode2
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep NetworksCode2
Learning Efficient Convolutional Networks through Network SlimmingCode2
Proximal Policy Optimization AlgorithmsCode2
Offline Model-Based Optimization: Comprehensive ReviewCode1
Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and InsightsCode1
MoRe Fine-Tuning with 10x Fewer ParametersCode1
TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series ClassificationCode1
NAS-BNN: Neural Architecture Search for Binary Neural NetworksCode1
einspace: Searching for Neural Architectures from Fundamental OperationsCode1
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language ModelsCode1
Differentiable Model Scaling using Differentiable TopkCode1
AFter: Attention-based Fusion Router for RGBT TrackingCode1
FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture SearchCode1
PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture SearchCode1
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion RecognitionCode1
Robustifying and Boosting Training-Free Neural Architecture SearchCode1
Multi-conditioned Graph Diffusion for Neural Architecture SearchCode1
On Latency Predictors for Neural Architecture SearchCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPOS (ProxylessNAS (GPU) latency)Accuracy75.3Unverified
2SPOS (FBNet-C latency)Accuracy75.1Unverified
3SPOS (block search + channel search)Accuracy74.7Unverified
4MUXNet-xsTop-1 Error Rate33.3Unverified
5FBNetV2-F1Top-1 Error Rate31.7Unverified
6LayerNAS-60MTop-1 Error Rate31Unverified
7NASGEPTop-1 Error Rate29.51Unverified
8MUXNet-sTop-1 Error Rate28.4Unverified
9NN-MASS-ATop-1 Error Rate27.1Unverified
10FBNetV2-F3Top-1 Error Rate26.8Unverified
#ModelMetricClaimedVerifiedStatus
1CR-LSOAccuracy (Test)46.98Unverified
2Shapley-NASAccuracy (Test)46.85Unverified
3β-SDARTS-RSAccuracy (Test)46.71Unverified
4β-RDARTS-L2Accuracy (Test)46.71Unverified
5NARAccuracy (Test)46.66Unverified
6ASE-NAS+Accuracy (Val)46.66Unverified
7BaLeNAS-TFAccuracy (Test)46.54Unverified
8AG-NetAccuracy (Test)46.42Unverified
9Local searchAccuracy (Test)46.38Unverified
10NASBOTAccuracy (Test)46.37Unverified
#ModelMetricClaimedVerifiedStatus
1Balanced MixtureAccuracy (% )91.55Unverified
2GDASTop-1 Error Rate3.4Unverified
3Bonsai-NetTop-1 Error Rate3.35Unverified
4Net2 (2)Top-1 Error Rate3.3Unverified
5μDARTSTop-1 Error Rate3.28Unverified
6NN-MASS- CIFAR-CTop-1 Error Rate3.18Unverified
7DARTS (first order)Top-1 Error Rate3Unverified
8NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
9AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified
10NASGEPTop-1 Error Rate2.82Unverified