# Generating Accurate Virtual Examples For Lifelong Machine Learning

## TL;DR

This paper introduces a method for generating virtual examples in lifelong machine learning by reconstructing data using a Restricted Boltzmann Machine, enabling knowledge retention without storing prior data.

## Contribution

It proposes a novel approach to create accurate virtual training examples through data reconstruction, addressing the retention challenge in lifelong learning without retaining old data.

## Key findings

- Reconstruction error from RBM can generate accurate virtual examples.
- A new measure compares dataset distributions based on reconstruction errors.
- The method improves knowledge consolidation in LML systems.

## Abstract

Lifelong machine learning (LML) is an area of machine learning research concerned with human-like persistent and cumulative nature of learning. LML system's objective is consolidating new information into an existing machine learning model without catastrophically disrupting the prior information. Our research addresses this LML retention problem for creating a knowledge consolidation network through task rehearsal without retaining the prior task's training examples. We discovered that the training data reconstruction error from a trained Restricted Boltzmann Machine can be successfully used to generate accurate virtual examples from the reconstructed set of a uniform random set of examples given to the trained model. We also defined a measure for comparing the probability distributions of two datasets given to a trained network model based on their reconstruction mean square errors.