<ul><li>This article reviews four major gradient descent-based architecture search methods for discovering the best neural architecture for image classification: DARTS, PDARTS, Fair DARTS, and Att-DARTS.</li><li>DARTS is a popular method that treats the architecture as a continuous variable and optimizes it using gradient-based methods. It uses a cell-based search space with operations such as convolutions, pooling, and identity.</li><li>DARTS learns the cell architecture by searching for the optimal combination of operations. It uses eight cells, including normal and reduction cells, to design high-performance neural networks.</li><li>The goal of DARTS is to learn the operation strength probabilities (α) and the optimal weights (ω) for constructing a cell and designing neural networks.</li></ul>

Understanding the Differential Architecture search for Automating the design of Neural Networks.

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