Neural Architecture Search
Aging Evolution
Introduced by Real et al. in Regularized Evolution for Image Classifier Architecture SearchAging Evolution, or Regularized Evolution, is an evolutionary algorithm for neural architecture search. Whereas in tournament selection, the best architectures are kept, in aging evolution we associate each genotype with an age, and bias the tournament selection to choose the younger genotypes. In the context of architecture search, aging evolution allows us to explore the search space more, instead of zooming in on good models too early, as non-aging evolution would.
Source: Regularized Evolution for Image Classifier Architecture Search
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Bayesian Optimization | 1 | 25.00% |
| Evolutionary Algorithms | 1 | 25.00% |
| Image Classification | 1 | 25.00% |
| Reinforcement Learning | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
