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 18011825 of 1915 papers

TitleStatusHype
DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification0
FACETS: Efficient Once-for-all Object Detection via Constrained Iterative Search0
Self-Supervised learning for Neural Architecture Search (NAS)0
Fairer and More Accurate Tabular Models Through NAS0
Self-supervised Neural Architecture Search0
FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences0
Farthest Greedy Path Sampling for Two-shot Recommender Search0
UDC: Unified DNAS for Compressible TinyML Models0
Multi-trial Neural Architecture Search with Lottery Tickets0
UFO-BLO: Unbiased First-Order Bilevel Optimization0
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification0
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation0
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model0
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking0
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning0
Fast Neural Architecture Construction using EnvelopeNets0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks0
DDFAD: Dataset Distillation Framework for Audio Data0
Ultrafast Photorealistic Style Transfer via Neural Architecture Search0
Fast Task-Aware Architecture Inference0
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs0
DC-NAS: Divide-and-Conquer Neural Architecture Search0
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends0
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run0
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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