Multi-Loss Weighting with Coefficient of Variations

• URL: http://arxiv.org/abs/2009.01717v2
• Date: Tue, 10 Nov 2020 10:41:03 GMT
• Title: Multi-Loss Weighting with Coefficient of Variations
• Authors: Rick Groenendijk, Sezer Karaoglu, Theo Gevers, Thomas Mensink
• Abstract summary: We propose a weighting scheme based on the coefficient of variations and set the weights based on properties observed while training the model. The proposed method incorporates a measure of uncertainty to balance the losses, and as a result the loss weights evolve during training without requiring another (learning based) optimisation. The validity of the approach is shown empirically for depth estimation and semantic segmentation on multiple datasets.
• Score: 19.37721431024278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many interesting tasks in machine learning and computer vision are learned by optimising an objective function defined as a weighted linear combination of multiple losses. The final performance is sensitive to choosing the correct (relative) weights for these losses. Finding a good set of weights is often done by adopting them into the set of hyper-parameters, which are set using an extensive grid search. This is computationally expensive. In this paper, we propose a weighting scheme based on the coefficient of variations and set the weights based on properties observed while training the model. The proposed method incorporates a measure of uncertainty to balance the losses, and as a result the loss weights evolve during training without requiring another (learning based) optimisation. In contrast to many loss weighting methods in literature, we focus on single-task multi-loss problems, such as monocular depth estimation and semantic segmentation, and show that multi-task approaches for loss weighting do not work on those single-tasks. The validity of the approach is shown empirically for depth estimation and semantic segmentation on multiple datasets.

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