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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1200 of 1915 papers

TitleStatusHype
Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition—0
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection—0
Revisiting Learning-based Video Motion Magnification for Real-time Processing—0
Revisiting Neural Architecture Search—0
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search—0
RHNAS: Realizable Hardware and Neural Architecture Search—0
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation—0
Robust 3D Face Alignment with Multi-Path Neural Architecture Search—0
Robust and Energy-efficient PPG-based Heart-Rate Monitoring—0
Robust NAS under adversarial training: benchmark, theory, and beyond—0
Robust Neural Architecture Search—0
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation—0
RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition—0
RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation—0
RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms—0
S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search—0
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search—0
Saliency-Aware Neural Architecture Search—0
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search—0
Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection—0
Sample-Efficient Neural Architecture Search by Learning Action Space—0
Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search—0
SASE: A Searching Architecture for Squeeze and Excitation Operations—0
Scalable NAS with Factorizable Architectural Parameters—0
Scalable Neural Architecture Search for 3D Medical Image Segmentation—0
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research—0
Scalable Reinforcement Learning-based Neural Architecture Search—0
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition—0
Scaling Up Neural Architecture Search with Big Single-Stage Models—0
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge—0
SCAN-Edge: Finding MobileNet-speed Hybrid Networks for Diverse Edge Devices via Hardware-Aware Evolutionary Search—0
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search—0
SDQ: Stochastic Differentiable Quantization with Mixed Precision—0
SEAL: Searching Expandable Architectures for Incremental Learning—0
Searching a High-Performance Feature Extractor for Text Recognition Network—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Searching Efficient Model-guided Deep Network for Image Denoising—0
Searching for Controllable Image Restoration Networks—0
Searching for Convolutions and a More Ambitious NAS—0
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs—0
Searching for Fast Model Families on Datacenter Accelerators—0
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks—0
Searching for Stage-wise Neural Graphs In the Limit—0
Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection—0
Searching for Two-Stream Models in Multivariate Space for Video Recognition—0
Searching the Deployable Convolution Neural Networks for GPUs—0
Searching to Sparsify Tensor Decomposition for N-ary Relational Data—0
Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification—0
Search-time Efficient Device Constraints-Aware Neural Architecture Search—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