# Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation

## TL;DR

This paper introduces a novel continual learning method that uses asymmetric loss approximation with overestimation to mitigate catastrophic forgetting, achieving near-optimal accuracy without additional network components.

## Contribution

It proposes a new asymmetric loss approximation technique for continual learning that overestimates unobserved task sides, improving scalability and accuracy.

## Key findings

- Achieves state-of-the-art accuracy on benchmark datasets.
- Effectively mitigates catastrophic forgetting.
- Operates without additional network components.

## Abstract

Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suffers from the requirements of additional network components and the limited scalability to a large number of tasks. We propose a novel approach to continual learning by approximating a true loss function using an asymmetric quadratic function with one of its sides overestimated. Our algorithm is motivated by the empirical observation that the network parameter updates affect the target loss functions asymmetrically. In the proposed continual learning framework, we estimate an asymmetric loss function for the tasks considered in the past through a proper overestimation of its unobserved sides in training new tasks, while deriving the accurate model parameter for the observable sides. In contrast to existing approaches, our method is free from the side effects and achieves the state-of-the-art accuracy that is even close to the upper-bound performance on several challenging benchmark datasets.