One-shot learning – overview

One-shot learning can be seen as an approach to train machines in a way that is similar to how humans learn. One-shot learning is an approach to learn a new task using limited supervised data with the help of strong prior knowledge. The first work published that resulted in high accuracy for the image classification problem dates back to the 2000s by Dr. Fei Fei Li—although, in recent years, researchers have made good progress tackling it through different deep learning architectures and optimization algorithms, such as matching networks, model agnostic meta-learning, and memory-augmented neural networks. One-shot learning has a lot of applications in several industries—the medical and manufacturing industries in particular. In medicine, we can use one-shot learning when there is limited data available, for example, when working...