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 11511175 of 1915 papers

TitleStatusHype
Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition0
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection0
Revisiting Learning-based Video Motion Magnification for Real-time Processing0
Revisiting Neural Architecture Search0
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search0
RHNAS: Realizable Hardware and Neural Architecture Search0
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation0
Robust 3D Face Alignment with Multi-Path Neural Architecture Search0
Robust and Energy-efficient PPG-based Heart-Rate Monitoring0
Robust NAS under adversarial training: benchmark, theory, and beyond0
Robust Neural Architecture Search0
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation0
RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition0
RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation0
RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms0
S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search0
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search0
Saliency-Aware Neural Architecture Search0
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search0
Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection0
Sample-Efficient Neural Architecture Search by Learning Action Space0
Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search0
SASE: A Searching Architecture for Squeeze and Excitation Operations0
Scalable NAS with Factorizable Architectural Parameters0
Scalable Neural Architecture Search for 3D Medical Image Segmentation0
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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-RDARTS-L2Accuracy (Test)46.71Unverified
4β-SDARTS-RSAccuracy (Test)46.71Unverified
5ASE-NAS+Accuracy (Val)46.66Unverified
6NARAccuracy (Test)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
7NN-MASS- CIFAR-ATop-1 Error Rate3Unverified
8DARTS (first order)Top-1 Error Rate3Unverified
9NASGEPTop-1 Error Rate2.82Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82Unverified