  Class imbalance is a common issue in real-world data distributions,
negatively impacting the training of accurate classifiers. Traditional
approaches to mitigate this problem fall into three main categories: class
re-balancing, information transfer, and representation learning. This paper
introduces a novel approach to enhance performance on long-tailed datasets by
optimizing the backbone architecture through neural architecture search (NAS).
Our research shows that an architecture's accuracy on a balanced dataset does
not reliably predict its performance on imbalanced datasets. This necessitates
a complete NAS run on long-tailed datasets, which can be computationally
expensive. To address this computational challenge, we focus on existing work,
called IMB-NAS, which proposes efficiently adapting a NAS super-network trained
on a balanced source dataset to an imbalanced target dataset. A detailed
description of the fundamental techniques for IMB-NAS is provided in this
paper, including NAS and architecture transfer. Among various adaptation
strategies, we find that the most effective approach is to retrain the linear
classification head with reweighted loss while keeping the backbone NAS
super-network trained on the balanced source dataset frozen. Finally, we
conducted a series of experiments on the imbalanced CIFAR dataset for
performance evaluation. Our conclusions are the same as those proposed in the
IMB-NAS paper.
