In some cases, a large amount of labeled data is unavailable. Therefore, some methods are proposed to solve the “small data” machine learning problems.
Few-shot learning tries to let the model classify new data that has seen only a few training samples. An extreme example is “one-shot” learning.
- Data-level approaches: Data augmentation, GAN;
- Parameter-level approaches: Regularization techniques, Meta-learning
A good tutorial can be found here.
In meta-learning, we use the knowledge from other unrelated (but similar?) models and tasks to help learn our model. The key idea is since different tasks (training and testing set, e.g., cat/dog, tree/car) are fed to the model (network), the model must learn how to classify different categories generally, instead of differentiating specific categories.
The approaches for meta-learning mainly rely on the utilization of prior knowledge either on similarity, learning, or data.