Weakly supervised learning learns from E with limited supervised data, such as incomplete, inaccurate, inexact or noisy supervised information.

University of Notre Dame

1)	Weakly Supervised Learning

2)	Imbalanced Learning

3)	Transfer Learning

4)	Meta Learning

Learning Problem Relevant to Few-Shot Learning

Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a few examples: A survey on few-shot learning. ACM computing surveys (csur), 53(3), 1-34.

Generalizing from a Few Examples: A Survey on Few-Shot Learning

Weakly Supervised Learning

Imbalanced learning is a machine learning setting where the algorithm learns from an experience dataset $$E$$ that is imbalanced with respect to the distribution of the target variable $$y$$.

Imbalanced Learning

Transfer Learning transfer the learned knowledge from one task/source with abundant training data to the current task/source with limited training data. 

Transfer Learning

Meta learning improves learning performance by first using meta-model to extract knowledge from meta data, and then aggregating meta models for the current task using task-specific information.

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