Aliasing refers to the phenomenon that high frequency signals degenerate into
completely different ones after sampling. It arises as a problem in the context
of deep learning as downsampling layers are widely adopted in deep
architectures to reduce parameters and computation. The standard solution is to
apply a low-pass filter (e.g., Gaussian blur) before downsampling. However, it
can be suboptimal to apply the same filter across the entire content, as the
frequency of feature maps can vary across both spatial locations and feature
channels. To tackle this, we propose an adaptive content-aware low-pass
filtering layer, which predicts separate filter weights for each spatial
location and channel group of the input feature maps. We investigate the
effectiveness and generalization of the proposed method across multiple tasks
including ImageNet classification, COCO instance segmentation, and Cityscapes
semantic segmentation. Qualitative and quantitative results demonstrate that
our approach effectively adapts to the different feature frequencies to avoid
aliasing while preserving useful information for recognition. Code is available
at this https URL

通过提供一个自适应、内容感知的低通滤波层，该文章提出了一种解决深度学习中降采样导致失真的问题的有效方法，并在多项任务中展示其效果，从而避免失真，同时保留用于识别的有用信息。