Anytime NAS can be applied to other types of datasets beyond tabular data by adapting the search space, search strategy, and architecture evaluation components to suit the specific characteristics of those datasets. For image data, the search space could include different convolutional neural network architectures with varying kernel sizes and depths. The search strategy might involve exploring transformations like rotations or flips for data augmentation. Architecture evaluation could focus on metrics like accuracy or F1 score for classification tasks.
By incorporating domain-specific knowledge into the design of Anytime NAS for different types of datasets, researchers can tailor the approach to efficiently explore a wide range of architectures within a given time budget while progressively improving performance as more resources become available. This adaptability allows Anytime NAS to address diverse machine learning tasks across various domains effectively.