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 | SPOS (ProxylessNAS (GPU) latency) | Accuracy | 75.3 | — | Unverified |
| 2 | SPOS (FBNet-C latency) | Accuracy | 75.1 | — | Unverified |
| 3 | SPOS (block search + channel search) | Accuracy | 74.7 | — | Unverified |
| 4 | MUXNet-xs | Top-1 Error Rate | 33.3 | — | Unverified |
| 5 | FBNetV2-F1 | Top-1 Error Rate | 31.7 | — | Unverified |
| 6 | LayerNAS-60M | Top-1 Error Rate | 31 | — | Unverified |
| 7 | NASGEP | Top-1 Error Rate | 29.51 | — | Unverified |
| 8 | MUXNet-s | Top-1 Error Rate | 28.4 | — | Unverified |
| 9 | NN-MASS-A | Top-1 Error Rate | 27.1 | — | Unverified |
| 10 | FBNetV2-F3 | Top-1 Error Rate | 26.8 | — | Unverified |