  In this paper, we attempt to address the challenge of applying Neural
Architecture Search (NAS) algorithms, specifically the Differentiable
Architecture Search (DARTS), to long-tailed datasets where class distribution
is highly imbalanced. We observe that traditional re-sampling and re-weighting
techniques, which are effective in standard classification tasks, lead to
performance degradation when combined with DARTS. To mitigate this, we propose
a novel adaptive learning rate scheduling strategy tailored for the
architecture parameters of DARTS when integrated with the Bilateral Branch
Network (BBN) for handling imbalanced datasets. Our approach dynamically
adjusts the learning rate of the architecture parameters based on the training
epoch, preventing the disruption of well-trained representations in the later
stages of training. Additionally, we explore the impact of branch mixing
factors on the algorithm's performance. Through extensive experiments on the
CIFAR-10 dataset with an artificially induced long-tailed distribution, we
demonstrate that our method achieves comparable accuracy to using DARTS alone.
And the experiment results suggest that re-sampling methods inherently harm the
performance of the DARTS algorithm. Our findings highlight the importance of
careful data augment when applying DNAS to imbalanced learning scenarios.
