  Recent advancements in artificial intelligence (AI) have positioned deep
learning (DL) as a pivotal technology in fields like computer vision, data
mining, and natural language processing. A critical factor in DL performance is
the selection of neural network architecture. Traditional predefined
architectures often fail to adapt to different data distributions, making it
challenging to achieve optimal performance. Neural architecture search (NAS)
offers a solution by automatically designing architectures tailored to specific
datasets. However, the effectiveness of NAS diminishes on long-tailed datasets,
where a few classes have abundant samples, and many have few, leading to biased
models.In this paper, we explore to improve the searching and training
performance of NAS on long-tailed datasets. Specifically, we first discuss the
related works about NAS and the deep learning method for long-tailed
datasets.Then, we focus on an existing work, called SSF-NAS, which integrates
the self-supervised learning and fair differentiable NAS to making NAS achieve
better performance on long-tailed datasets.An detailed description about the
fundamental techniques for SSF-NAS is provided in this paper, including DARTS,
FairDARTS, and Barlow Twins. Finally, we conducted a series of experiments on
the CIFAR10-LT dataset for performance evaluation, where the results are align
with our expectation.
