Novel meta-learning approaches for few-shot image classification - Song, Heda (2022)
In recent years, there has been rapid progress in computing performance and communication techniques, leading to a surging interest in artificial intelligence. Artificial intelligence aims to achieve human intelligence by making a machine think. However, current machine learning and optimisation techniques are far from fully accomplishing this, suffering from several limitations. For example, humans can learn a new concept quickly from very few examples, while artificial intelligence algorithms usually require a large number of examples to extract useful patterns. To tackle this issue, the computer science community has recently delved into the challenge of learning from very limited data, also known as few-shot learning.