Transfer Learning Approaches for Small-Scale Datasets

Abstract

Transfer learning has become an effective paradigm in machine learning and deep learning, especially in the case when labeled data are limited. Small scale data are a big challenge to the conventional deep learning models since they overfit, are not able to generalize, and do not learn features well. Transfer learning helps to address these problems by using the information of large domains in the source task to facilitate activities in target domains with scarce data. In this paper, the researcher will take the task of thoroughly examining transfer learning methods with small-scale datasets in mind. Our view of the base concepts, architecture, and strategies of domain adaptation techniques, which make the reuse of pretrained models effective are analyzed. The literature review carried out in the paper examines available literature indicating supervised, unsupervised, and semi-supervised transfer learning techniques. In addition, we suggest a systematic approach to the execution of transfer learning pipelines such as feature extraction, fine-tuning schemes, regularization schemes and metrics of evaluation. The practical experience and performance evaluation shows that, transfer learning leads to a high convergence speed, accurate classification, and robustness, in comparison to scratch training. There are negative transfer, domain-shift, and model-selection challenges which are discussed too. Lastly, the paper discusses future research directions in the area of self-supervised learning, few-shot learning, and adaptive transfer mechanisms in the regimes of small data. The results confirm that transfer learning is a core solution to the real-world problems that may be limited by available labeled data.