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

• URL: http://arxiv.org/abs/2210.16003v1
• Date: Fri, 28 Oct 2022 09:10:10 GMT
• Title: Evaluating the Impact of Loss Function Variation in Deep Learning for Classification
• Authors: Simon Dr\"ager, Jannik Dunkelau
• Abstract summary: The loss function is arguably among the most important hyper parameters for a neural network. 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 can outperform the State-of-the-Art choices significantly.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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