  Designing proper loss functions for vision tasks has been a long-standing
research direction to advance the capability of existing models. For object
detection, the well-established classification and regression loss functions
have been carefully designed by considering diverse learning challenges.
Inspired by the recent progress in network architecture search, it is
interesting to explore the possibility of discovering new loss function
formulations via directly searching the primitive operation combinations. So
that the learned losses not only fit for diverse object detection challenges to
alleviate huge human efforts, but also have better alignment with evaluation
metric and good mathematical convergence property. Beyond the previous
auto-loss works on face recognition and image classification, our work makes
the first attempt to discover new loss functions for the challenging object
detection from primitive operation levels. We propose an effective
convergence-simulation driven evolutionary search algorithm, called
CSE-Autoloss, for speeding up the search progress by regularizing the
mathematical rationality of loss candidates via convergence property
verification and model optimization simulation. CSE-Autoloss involves the
search space that cover a wide range of the possible variants of existing
losses and discovers best-searched loss function combination within a short
time (around 1.5 wall-clock days). We conduct extensive evaluations of loss
function search on popular detectors and validate the good generalization
capability of searched losses across diverse architectures and datasets. Our
experiments show that the best-discovered loss function combinations outperform
default combinations by 1.1% and 0.8% in terms of mAP for two-stage and
one-stage detectors on COCO respectively. Our searched losses are available at
https://github.com/PerdonLiu/CSE-Autoloss.
