Gradient Episodic Memory for Continual Learning

Group Member

Yu Xuan Lee, Tsen Yee Heng

Background and Introduction

Supervised learning consist of a training set [math]\displaystyle{ D_{tx}=(x_i,y_i)^n_{i=1} }[/math], where [math]\displaystyle{ x_i \in X }[/math] and [math]\displaystyle{ y_i \in Y }[/math]. Empirical Risk Minimization (ERM) is one of the common supervised learning method used to minimize a loss function by having multiple passes over the training set.

[math]\displaystyle{ \frac{1}{|D_{tr}|}\textstyle \sum_{x_i,y_i} \in D_{tr} \ell (f(x_i),y_i) }[/math]

where [math]\displaystyle{ \ell :\mathcal {Y} \times \mathcal {Y} \to [0, \infty) }[/math]

However, the downside of ERM is "catastrophic forgetting", which is the problem of recalling past knowledge upon acquiring new ones.

Gradient Episodic Memory (GEM) is a continual learning model that alleviates forgetting on previous acquired knowledge, while solving new problems more efficiently.