  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).
