# Encoder Based Lifelong Learning

## TL;DR

This paper presents an autoencoder-based lifelong learning method that mitigates catastrophic forgetting by preserving task-specific features, enabling a single model to learn multiple tasks sequentially with reduced performance loss.

## Contribution

Introduces a novel autoencoder-based approach for lifelong learning that preserves previous task knowledge while allowing adaptation to new tasks.

## Key findings

- Reduces catastrophic forgetting compared to existing methods
- Effective on image classification tasks
- Maintains performance on previous tasks while learning new ones

## Abstract

This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catastrophic forgetting: as they tend to adapt to the most recently seen task, they lose performance on the tasks that were learned previously. Our method aims at preserving the knowledge of the previous tasks while learning a new one by using autoencoders. For each task, an under-complete autoencoder is learned, capturing the features that are crucial for its achievement. When a new task is presented to the system, we prevent the reconstructions of the features with these autoencoders from changing, which has the effect of preserving the information on which the previous tasks are mainly relying. At the same time, the features are given space to adjust to the most recent environment as only their projection into a low dimension submanifold is controlled. The proposed system is evaluated on image classification tasks and shows a reduction of forgetting over the state-of-the-art