Learning from Few Examples: A Summary of Approaches to Few-Shot Learning
It provides a summary of approaches for researchers and practitioners dealing with data availability issues, but it is incremental as it reviews existing methods without introducing new techniques.
This survey paper compiles and discusses recent algorithms for few-shot learning, which aims to learn patterns from limited data to address data scarcity and high computational costs in deep learning.
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learning solutions suffer from data hunger and extensively high computation time and resources. Furthermore, data is often not available due to not only the nature of the problem or privacy concerns but also the cost of data preparation. Data collection, preprocessing, and labeling are strenuous human tasks. Therefore, few-shot learning that could drastically reduce the turnaround time of building machine learning applications emerges as a low-cost solution. This survey paper comprises a representative list of recently proposed few-shot learning algorithms. Given the learning dynamics and characteristics, the approaches to few-shot learning problems are discussed in the perspectives of meta-learning, transfer learning, and hybrid approaches (i.e., different variations of the few-shot learning problem).