In this work, we propose to progressively increase the training difficulty during learning a neural network model via a novel strategy which we call mini-batch trimming. This strategy makes sure that the optimizer puts its focus in the later training stages on the more difficult samples, which we identify as the ones with the highest loss in the current mini-batch. The strategy is very easy to integrate into an existing training pipeline and does not necessitate a change of the network model. Experiments on several image classification problems show that mini-batch trimming is able to increase the generalization ability (measured via final test error) of the trained model.

翻译：在这项工作中,我们建议通过我们称之为微型批量裁剪的新战略,在学习神经网络模型的过程中逐步增加培训难度。这个战略确保优化器在后期培训阶段将重点放在较困难的样本上,我们确定这些样本是目前微型批量损失最大的样本。这个战略很容易融入现有的培训管道,不需要改变网络模型。关于几个图像分类问题的实验表明,小型批量剪裁能够提高(通过最终测试错误衡量的)经过培训的模式的普及能力。