Gradient-based learning for F-measure and other performance metrics
Many important classification performance metrics, e.g. $F$-measure, are non-differentiable and non-decomposable, and are thus unfriendly to gradient descent algorithm. Consequently, despite their popularity as evaluation metrics, these metrics are rarely optimized as training objectives in neural network community. In this paper, we propose an empirical utility maximization scheme with provable learning guarantees to address the non-differentiability of these metrics. We then derive a strongly consistent gradient estimator to handle non-decomposability. These innovations enable end-to-end optimization of these metrics with the same computational complexity as optimizing a decomposable and differentiable metric, e.g. cross-entropy loss.
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CIFAR-10
CIFAR-100
