NasNet Architecture

NasNet Architecture is a type of neural network architecture that uses a technique called Neural Architecture Search (NAS) to automatically design the best neural network structure for a given task. This process involves searching through a large space of possible network structures to find the most efficient and effective one. An example of NasNet Architecture is the NasNet-A model, which was developed by Google’s DeepMind team. This model achieved state-of-the-art performance on image classification tasks, outperforming human-designed networks. One verifiable fact is that NasNet-A achieved a top-1 accuracy of 82.7% on the ImageNet dataset, surpassing the previous state-of-the-art performance at the time. An analogy to understand NasNet Architecture is like having a computer program that can automatically design the most efficient and effective blueprint for a building, without human intervention. Just like how NasNet Architecture can automatically design the best neural network structure for a given task.