Many modern machine learning applications come with complex and nuanced
design goals such as minimizing the worst-case error, satisfying a given
precision or recall target, or enforcing group-fairness constraints. Popular
techniques for optimizing such non-decomposable objectives reduce the problem
into a sequence of cost-sensitive learning tasks, each of which is then solved
by re-weighting the training loss with example-specific costs. We point out
that the standard approach of re-weighting the loss to incorporate label costs
can produce unsatisfactory results when used to train over-parameterized
models. As a remedy, we propose new cost-sensitive losses that extend the
classical idea of logit adjustment to handle more general cost matrices. Our
losses are calibrated, and can be further improved with distilled labels from a
teacher model. Through experiments on benchmark image datasets, we showcase the
effectiveness of our approach in training ResNet models with common robust and
constrained optimization objectives.

本文提出一种新的代价敏感损失函数，以解决标签代价权重重新加权训练在过度参数化模型中可能导致的问题，并进行了 ResNet 模型的实验验证。