Meta-Learning Loss Functions for Deep Neural Networks

TL;DR

This paper explores meta-learning approaches to optimize loss functions in deep neural networks, aiming to enable faster learning with fewer examples by embedding prior knowledge into the loss design.

Contribution

It introduces a novel perspective on meta-learning by focusing on the loss function itself, which has been less studied compared to optimizers and initializations.

Findings

Meta-learning loss functions improves sample efficiency.

Enhanced performance on few-shot learning tasks.

Provides a framework for designing task-specific loss functions.

Abstract

Humans can often quickly and efficiently solve complex new learning tasks given only a small set of examples. In contrast, modern artificially intelligent systems often require thousands or millions of observations in order to solve even the most basic tasks. Meta-learning aims to resolve this issue by leveraging past experiences from similar learning tasks to embed the appropriate inductive biases into the learning system. Historically methods for meta-learning components such as optimizers, parameter initializations, and more have led to significant performance increases. This thesis aims to explore the concept of meta-learning to improve performance, through the often-overlooked component of the loss function. The loss function is a vital component of a learning system, as it represents the primary learning objective, where success is determined and quantified by the system's ability to optimize for that objective successfully.