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 1–10 of 1915 papers
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
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Balanced Mixture | Accuracy (% ) | 79.61 | — | Unverified |
| 2 | μDARTS | Percentage Error | 19.39 | — | Unverified |
| 3 | NASGEP | Percentage Error | 18.83 | — | Unverified |
| 4 | DARTS-PRIME | Percentage Error | 17.44 | — | Unverified |
| 5 | DU-DARTS | Percentage Error | 16.74 | — | Unverified |
| 6 | β-DARTS | Percentage Error | 16.52 | — | Unverified |
| 7 | ZenNet-2.0M | Percentage Error | 15.6 | — | Unverified |
| 8 | NAT-M1 | Percentage Error | 14 | — | Unverified |
| 9 | MUXNet-m | Percentage Error | 13.9 | — | Unverified |
| 10 | NAT-M2 | Percentage Error | 12.5 | — | Unverified |