  Cross-entropy loss and focal loss are the most common choices when training
deep neural networks for classification problems. Generally speaking, however,
a good loss function can take on much more flexible forms, and should be
tailored for different tasks and datasets. Motivated by how functions can be
approximated via Taylor expansion, we propose a simple framework, named
PolyLoss, to view and design loss functions as a linear combination of
polynomial functions. Our PolyLoss allows the importance of different
polynomial bases to be easily adjusted depending on the targeting tasks and
datasets, while naturally subsuming the aforementioned cross-entropy loss and
focal loss as special cases. Extensive experimental results show that the
optimal choice within the PolyLoss is indeed dependent on the task and dataset.
Simply by introducing one extra hyperparameter and adding one line of code, our
Poly-1 formulation outperforms the cross-entropy loss and focal loss on 2D
image classification, instance segmentation, object detection, and 3D object
detection tasks, sometimes by a large margin.
