Fair Differentiable Neural Network Architecture Search for Long-Tailed Data with Self-Supervised Learning
This addresses the issue of biased models in NAS for long-tailed data, which is incremental as it builds on existing methods like SSF-NAS.
The paper tackles the problem of neural architecture search (NAS) performing poorly on long-tailed datasets by integrating self-supervised learning with fair differentiable NAS, achieving results that align with expectations on the CIFAR10-LT dataset.
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.