Prior knowledge can be used to augment the training set, and below are several ways of achieving it:

1.	Transforming Samples from Training Set

2.	Transforming Samples from a Weakly Labeled or Unlabeled Data Set

3.	Transforming Samples from Similar Data Sets


Ways of Augmenting Data of Few-Shot Learning

1)	Multitask Learning

2)	Embedding Learning

3)	Learning with External Memory

4)	Generative Modeling


Types of Few Shot Learning by Model

In terms of how searching strategy is affected by prior knowledge, searching algorithm motivated few-shot learning methods can be categorized as:

1)	Refining existing parameters

2)	Refining meta-learned parameters.

3)	Learning the optimizer


Types of few-shot learning by algorithms

Few-Shot Learning can be divided by what part that prior knowledge has been added to:

1)	Data: The training data is augmented by prior knowledge

2)	Model: The possible model space is constrained according to prior knowledge

3)	Algorithm: The search strategy in hypothesis model space has been improved.

University of Notre Dame

Real-world relation distributions are long-tail. This means only the common relations obtain sufficient training instances and most relations have very limited relational facts and corresponding sentences. Few-shot learning focuses on grasping tasks with only a few training examples and is a good fit for this need. The general idea of few-shot models is to train good representations of instances or learn ways of fast adaptation from existing large-scale data, and then transfer to new tasks.

Let T be some task, E be past experience related to this classes of task T, and P be performance measure of T. Few-Shot learning is a type of machine learning problem which is specified by E, T, and P, but only has limited E either quantitatively or qualitatively. E typically includes supervised information and prior knowledge.

Performing more efficient learning with few-shot learning for RE

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

There are mainly two ways for handling few-shot learning for this purpose:
- metric learning
- meta learning

Ways for handling few-shot learning for RE

1.	Few-Shot Classification learns a classification function $\hat{h}$ and output a class label $y_i$ for each predictor $x_i$.
2.	Few-Shot Regression learns a regression function $\hat{h}$ and output a continuous value $y_i$ for each predictor $x_i$.
3.	Few-Shot Reinforcement Learning learns a policy from a trajectory of state-action pairs.


Types of Few-Shot Learning:

1)	Weakly Supervised Learning

2)	Imbalanced Learning

3)	Transfer Learning

4)	Meta Learning

Learning Problem Relevant to Few-Shot Learning

Let $h$ be the hypothetical model, $x$ be predictors, $y$ be predicted variable, $p(x,y)$ be joint probability density of $x$ and $y$, then the Expected Risk with respect to $h$ can be defined as $R(h)=\mathbf{E}[l(h(x), y)]$.

Because $p(x,y)$ is unknown, expected risk can be estimated by Empirical Risk, which is defined as $R_I(h)=\frac{1}{I}\sum_{i=1}^{I} l(h(x_i), y_i)$.


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