Neural Architecture Search and Transfer Learning
Transfer learning, an alternative AutoML strategy, involves repurposing a pre-trained model, initially developed for one task, as a starting point for a new problem. The rationale behind this method is that neural architectures trained on sufficiently large datasets can act as general models with applicability to similar problems. In deep learning, transfer learning is widely adopted because learned feature maps can be leveraged to train deep neural networks (DNNs) with limited data.
In contrast, NAS posits that each dataset, along with its specific hardware and production environment, demands a unique architecture optimized for its performance. Unlike transfer learning, NAS offers customization and flexibility, necessitating data scientists and developers to learn and train weights for the new architecture.
Ultimately, the choice between these AutoML approaches hinges on the specific use case and available resources.
Neural Architecture Search Algorithm
Neural Architecture Search (NAS) falls within the realm of automated machine learning (AutoML). AutoML is a comprehensive term encompassing the automation of diverse tasks in the application of machine learning to real-world challenges. The article explores the fundamentals, and applications of the NAS algorithm.
Table of Content
- What is Neural Architecture Search?
- Components of Neural Architecture Search
- Neural Architecture Search and Transfer Learning
- Applications of Neural Architecture Search(NAS)
- Advantages and Disadvantages of Neural Architecture Search