As deep neural networks include a high number of parameters and operations, it can be a challenge to implement these models on devices with limited computational resources. Despite the development of novel pruning methods toward resource-efficient models, it has become evident that these models are not capable of handling "imbalanced" and "limited number of data points". We proposed a novel filter pruning method by considering the input and output of filters along with the values of the filters that deal with imbalanced datasets better than others. Our pruning method considers the fact that all information about the importance of a filter may not be reflected in the value of the filter. Instead, it is reflected in the changes made to the data after the filter is applied to it. In this work, three methods are compared with the same training conditions except for the ranking values of each method, and 14 methods are compared from other papers. We demonstrated that our model performed significantly better than other methods for imbalanced medical datasets. For example, when we removed up to 58% of FLOPs for the IDRID dataset and up to 45% for the ISIC dataset, our model was able to yield an equivalent (or even superior) result to the baseline model. To evaluate FLOP and parameter reduction using our model in real-world settings, we built a smartphone app, where we demonstrated a reduction of up to 79% in memory usage and 72% in prediction time. All codes and parameters for training different models are available at https://github.com/mohofar/Beta-Rank

翻译：深度神经网络包含大量参数和计算操作，因此将此类模型部署到计算资源受限的设备上具有挑战性。尽管针对资源高效模型提出了新的剪枝方法，但这些方法显然无法处理“不平衡”和“有限数据点”的情况。我们提出了一种新颖的滤波器剪枝方法，该方法综合考虑滤波器的输入、输出及权重值，能比其他方法更好地处理不平衡数据集。我们的剪枝方法认为，滤波器重要性的全部信息可能并非体现在其权重值中，而是体现在滤波器应用于数据后所引起的数据变化上。本研究在相同训练条件下（仅各方法的排序值不同）比较了三种方法，并与来自其他论文的十四种方法进行对比。我们证明，对于不平衡医学数据集，本模型性能显著优于其他方法。例如，当从IDRID数据集中移除高达58%的浮点运算量、从ISIC数据集中移除高达45%的浮点运算量时，本模型仍能获得与基线模型相当（甚至更优）的结果。为评估本模型在实际场景中浮点运算量与参数缩减的效果，我们开发了一款智能手机应用程序，结果显示内存使用量最多减少79%，预测时间最多减少72%。训练不同模型的所有代码和参数均可在 https://github.com/mohofar/Beta-Rank 获取。