MetaModelNet Based Dynamic Transfer Learning Framework for Long Tail Recognition

Long-tail recognition is a situation where the classes or categories in a given dataset are distributed in the same way as the long-tail. This implies that there are a few programs that have many instances and many classes with few instances (i.e. the head and tail classes respectively). In this article, we present MetaModelNet, a new framework that is designed to tackle the problems of long-tail identification in the computer vision field. MetaModelNet also incorporates advanced deep learning techniques in data acquisition, testing, and statistical analysis for the dynamic transfer learning of head classes with many samples to tail classes with fewer samples. MetaModelNet guarantees high accuracy on different datasets such as SUN-397, Places, and ImageNet due to proper design of the architecture. This involves adjusting the hyperparameters as well as assessing the measures for long-tail identification. MetaModelNet has been validated by comprehensive ablation testing and comparative analysis of the dynamic transfer learning efficacy and versatility. Therefore, from these tests, it has been observed that MetaModelNet has a higher recognition accuracy compared to the current methods. Furthermore, the detailed analysis of the relationships within the given model will contribute to the identification of new perspectives on the investigation of the regularization procedures and adaptive learning algorithms. This has the potential for the development of recognition systems that are capable of dealing with imbalanced data in various real-life applications in a stable and efficient manner.