Current state-of-the-art class-imbalanced loss functions for deep models require exhaustive tuning on hyperparameters for high model performance, resulting in low training efficiency and impracticality for nonexpert users. To tackle this issue, a parameter-free loss (PF-loss) function is proposed, which works for both binary and multiclass-imbalanced deep learning for image classification tasks. PF-loss provides three advantages: 1) training time is significantly reduced due to NO tuning on hyperparameter(s); 2) it dynamically pays more attention on minority classes (rather than outliers compared to the existing loss functions) with NO hyperparameters in the loss function; and 3) higher accuracy can be achieved since it adapts to the changes of data distribution in each mini-batch instead of the fixed hyperparameters in the existing methods during training, especially when the data are highly skewed. Experimental results on some classical image datasets with different imbalance ratios (IR, up to 200) show that PF-loss reduces the training time down to 1/148 of that spent by compared state-of-the-art losses and simultaneously achieves comparable or even higher accuracy in terms of both G-mean and area under receiver operating characteristic (ROC) curve (AUC) metrics, especially when the data are highly skewed.

中文翻译：

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图像分类中类不平衡深度学习的无参数损失


当前最先进的深度模型类不平衡损失函数需要对超参数进行详尽的调整才能获得高模型性能，从而导致训练效率低下并且对于非专家用户来说不切实际。为了解决这个问题，提出了一种无参数损失（PF-loss）函数，它适用于图像分类任务的二元和多类不平衡深度学习。 PF-loss 提供了三个优点：1）由于无需调整超参数，训练时间显着减少； 2）它动态地更多地关注少数类（而不是与现有损失函数相比的异常值），损失函数中没有超参数； 3）在训练过程中，它适应每个小批量中数据分布的变化，而不是现有方法中的固定超参数，因此可以获得更高的准确率，特别是当数据高度倾斜时。在具有不同不平衡比（IR，高达 200）的一些经典图像数据集上的实验结果表明，PF-loss 将训练时间减少至与最先进损失相比的 1/148，同时实现了可比较或在 G 均值和受试者工作特征 (ROC) 曲线下面积 (AUC) 指标方面具有更高的准确性，特别是当数据高度倾斜时。