Long-tailed datasets, where head classes comprise much more training samples than tail classes, cause recognition models to get biased towards the head classes. Weighted loss is one of the most popular ways of mitigating this issue, and a recent work has suggested that class-difficulty might be a better clue than conventionally used class-frequency to decide the distribution of weights. A heuristic formulation was used in the previous work for quantifying the difficulty, but we empirically find that the optimal formulation varies depending on the characteristics of datasets. Therefore, we propose Difficulty-Net, which learns to predict the difficulty of classes using the model's performance in a meta-learning framework. To make it learn reasonable difficulty of a class within the context of other classes, we newly introduce two key concepts, namely the relative difficulty and the driver loss. The former helps Difficulty-Net take other classes into account when calculating difficulty of a class, while the latter is indispensable for guiding the learning to a meaningful direction. Extensive experiments on popular long-tailed datasets demonstrated the effectiveness of the proposed method, and it achieved state-of-the-art performance on multiple long-tailed datasets.
翻译:长尾类数据集( 长尾类由更多的培训样本组成) 长尾类数据集( 长尾类由更多的培训样本组成) 导致识别模型偏向于头类。 加权损失是缓解这一问题最受欢迎的方法之一, 最近的一项工作表明, 类困难比传统使用的类频率更能提供线索, 以决定重量分布。 在先前的工作中使用了一种超常的配方来量化难度, 但我们从经验上发现, 最佳配方因数据集的特性而异。 因此, 我们提议了“ 难网 ”, 以学习用模型在元学习框架中的性能预测班级的难度。 为了在其它类中学习一个类的合理困难, 我们新引入了两个关键概念, 即相对困难和驱动器损失。 前者有助于在计算一个类的难度时将其他类考虑在内, 而后者对于指导学习走向有意义的方向是不可或缺的。 在广受欢迎的长尾类数据集上进行广泛的实验, 展示了拟议方法的有效性, 并且它实现了多尾次的状态性能。