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The cross entropy loss is widely used due to its effectiveness and solidtheoretical grounding. However, as training progresses, the loss tends to focuson hard to classify samples, which may prevent the network from obtaining gainsin performance. While most work in the field suggest ways to classify hardnegatives, we suggest to strategically leave hard negatives behind, in order tofocus on misclassified samples with higher probabilities. We show that addingto the optimization goal the expectation loss, which is a better approximationof the zero-one loss, helps the network to achieve better accuracy. We,therefore, propose to shift between the two losses during training, focusingmore on the expectation loss gradually during the later stages of training. Ourexperiments show that the new training protocol improves performance across adiverse set of classification domains, including computer vision, naturallanguage processing, tabular data, and sequences. Our code and scripts areavailable at supplementary.

Mixing between the Cross Entropy and the Expectation Loss Terms