Neural networks often suffer from a feature preference problem, where they tend to overly rely on specific features to solve a task while disregarding other features, even if those neglected features are essential for the task. Feature preference problems have primarily been investigated in classification task. However, we observe that feature preference occurs in high-dimensional regression task, specifically, source separation. To mitigate feature preference in source separation, we propose FEAture BAlancing by Suppressing Easy feature (FEABASE). This approach enables efficient data utilization by learning hidden information about the neglected feature. We evaluate our method in a multi-channel source separation task, where feature preference between spatial feature and timbre feature appears.

翻译：神经网络常面临特征偏好问题，即网络倾向于过度依赖特定特征来完成任务，而忽略其他即使对任务至关重要的特征。特征偏好问题主要在分类任务中得到研究。然而，我们观察到在高维回归任务，特别是声音源分离中，特征偏好现象同样存在。为缓解源分离中的特征偏好，我们提出了通过抑制简单特征实现特征平衡的方法（FEABASE）。该方法通过学习被忽略特征中的隐藏信息，实现数据的高效利用。我们在多通道源分离任务中评估了该方法，该任务中空间特征与音色特征之间存在特征偏好。