Progressive Neural Architecture Search

The Progressive Neural Architecture Search (PNAS) is a revolutionary method that facilitates CNN learning. This strategy utilizes sequential model-based optimization to discover the structure of CNNs.

What is PNAS?

PNAS stands for Progressive Neural Architecture Search, a technique designed to aid in the learning of the convolutional neural network architecture. The method deploys a scientific strategy termed Sequential Model-Based Optimization (SMBO) to investigate the cell structure. This technique involves beginning with very basic neural network models and gradually exploring more sophisticated ones. Its primary goal is to detect and remove any unproductive structure in the process.

How Does PNAS Work?

At the beginning of each iteration, the algorithm starts with a group of K candidate cells, each of size b. These cells are then trained and assessed using a dataset of interest. Such procedures can be quite expensive, which is why PNAS employs a surrogate model to predict a structure's performance without the need for actual training.

Afterwards, the K candidates of size b are expanded into K prime (K') children, each of size b+1. Using the surrogate function, all the K' children are ranked, with the first K being handpicked and subsequently trained and assessed. This cycle continues until the max number of blocks needed in a cell, B, is reached.

Why is PNAS Important?

PNAS demonstrates a significant leap in the capabilities of CNNs by allowing them to learn with lessened human intervention. By carefully studying the CNN structure, this method eliminates inefficient networks which would have slowed down the learning process, thus improving the performance of the AI model.

In summary, PNAS is a computational method that uses SMBO to search for the optimal structure of CNNs. With an optimized model, AI learning becomes more efficient, and CNNs can be trained faster and more accurately.

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