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：神经网络常受特征偏好问题困扰，即倾向于过度依赖特定特征来解决任务，而忽视其他特征，即使这些被忽略的特征对任务至关重要。特征偏好问题主要在分类任务中得到研究。然而，我们观察到特征偏好同样出现在高维回归任务中，具体而言是源分离。为缓解源分离中的特征偏好问题，我们提出通过抑制易特征进行特征平衡（FEABASE）方法。该方法通过学习被忽略特征的隐藏信息，实现高效的数据利用。我们在多通道源分离任务中评估该方法，该任务中空间特征与音色特征之间存在特征偏好。