Evaluating the Impact of Loss Function Variation in Deep Learning for Classification

Simon Dräger; Jannik Dunkelau

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arXiv · 2210.16003

Evaluating the Impact of Loss Function Variation in Deep Learning for Classification

Simon Dräger·Jannik Dunkelau

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Abstract

The loss function is arguably among the most important hyperparameters for a neural network. Many loss functions have been designed to date, making a correct choice nontrivial. However, elaborate justifications regarding the choice of the loss function are not made in related work. This is, as we see it, an indication of a dogmatic mindset in the deep learning community which lacks empirical foundation. In this work, we consider deep neural networks in a supervised classification setting and analyze the impact the choice of loss function has onto the training result. While certain loss functions perform suboptimally, our work empirically shows that under-represented losses such as the KL Divergence can outperform the State-of-the-Art choices significantly, highlighting the need to include the loss function as a tuned hyperparameter rather than a fixed choice.

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BibTeX RIS

Simon Dräger, Jannik Dunkelau. 2022-10-28. Evaluating the Impact of Loss Function Variation in Deep Learning for Classification. https://arxiv.org/abs/2210.16003

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