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 1601–1650 of 1915 papers

TitleStatusHype
Efficient Differentiable Neural Architecture Search with Meta Kernels—0
Ultrafast Photorealistic Style Transfer via Neural Architecture Search—0
AdversarialNAS: Adversarial Neural Architecture Search for GANsCode0
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image ClassificationCode1
EDAS: Efficient and Differentiable Architecture Search—0
DEGAS: Differentiable Efficient Generator Search—0
Neural Predictor for Neural Architecture SearchCode0
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning—0
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification—0
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection—0
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural NetworksCode0
DATA: Differentiable ArchiTecture ApproximationCode0
SGAS: Sequential Greedy Architecture SearchCode0
Blockwisely Supervised Neural Architecture Search with Knowledge DistillationCode1
Towards Oracle Knowledge Distillation with Neural Architecture Search—0
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture SearchCode0
Ranking architectures using meta-learning—0
Binarized Neural Architecture Search—0
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial AttacksCode0
Meta-Learning of Neural Architectures for Few-Shot LearningCode1
Exploiting Operation Importance for Differentiable Neural Architecture Search—0
Measuring Uncertainty through Bayesian Learning of Deep Neural Network StructureCode0
SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection—0
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS—0
Data Proxy Generation for Fast and Efficient Neural Architecture Search—0
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural ArchitectureCode0
Search to Distill: Pearls are Everywhere but not the Eyes—0
Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition—0
ImmuNeCS: Neural Committee Search by an Artificial Immune System—0
Fine-Grained Neural Architecture Search—0
Interstellar: Searching Recurrent Architecture for Knowledge Graph EmbeddingCode1
S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search—0
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification—0
Neural Architecture Search for Natural Language Understanding—0
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural SearchingCode0
Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture SearchCode1
Learning to reinforcement learn for Neural Architecture SearchCode0
RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search—0
Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter TuningCode0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
On Neural Architecture Search for Resource-Constrained Hardware Platforms—0
Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators—0
NAT: Neural Architecture Transformer for Accurate and Compact ArchitecturesCode1
Fast Hardware-Aware Neural Architecture Search—0
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture SearchCode1
Stabilizing DARTS with Amended Gradient Estimation on Architectural ParametersCode0
Efficient Decoupled Neural Architecture Search by Structure and Operation SamplingCode0
NASIB: Neural Architecture Search withIn Budget—0
Structural Analysis of Sparse Neural Networks—0
One-Shot Neural Architecture Search via Self-Evaluated Template NetworkCode0
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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-RDARTS-L2Accuracy (Test)46.71—Unverified
4β-SDARTS-RSAccuracy (Test)46.71—Unverified
5ASE-NAS+Accuracy (Val)46.66—Unverified
6NARAccuracy (Test)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
7NN-MASS- CIFAR-ATop-1 Error Rate3—Unverified
8DARTS (first order)Top-1 Error Rate3—Unverified
9NASGEPTop-1 Error Rate2.82—Unverified
10AlphaX-1 (cutout NASNet)Top-1 Error Rate2.82—Unverified