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卷积作为CNN的基础部件之一，尽管常用，但存在两个主要缺陷：(1) 内容不可知；(2) 重度计算量。动态滤波器具有内容自适应特性，但同时进一步提升了计算量。深度(depth-wise)卷积是一种轻量型版本，但它往往会造成CNN性能下降，或者需要更大的通道数。
本文提出一种解耦动态滤波器(Decoupled Dynamic Filter)，它可以同时解决上述缺陷。受启发于近期注意力的进展，DDF将深度动态滤波器解耦为空域与通道动态滤波器。该分解可以大大减少参数量，并将计算量限制在与深度卷积同等水平。同时，采用DDF替换分类网络中的标准卷积可以带来显著的性能提升。比如，ResNet50/101分别可以带来1.9%与1.3%的top1精度提升，且计算量近乎减半。在检测与联合上采样方面的实验同样证实了DDF上采样变种相比标准卷积的优异性。
本文所提DDF及其上采样变种DDF-Up具有以下几点优异属性：
Content-adaptive DDF提供了空间可变滤波器，这使得其具有内容自适应特性； Fast runtime DDF具有与深度卷积相近的计算量，因此它的推理速度要比标准卷积、动态滤波器更快； Smaller memory footprint DDF可以显著降低动态滤波器的内存占用，这使得我们可以采用DDF直接替换所有的标准卷积； Consistent performance improvements 采用DDF/DDF-Up替换标准卷积可以带来一致性的性能提升，同时在不同网络、不同任务上均取得了SOTA性能。
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Decoupled Dynamic Filter Networks
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This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented in this paper are a dual-stream, 3D convolutional autoencoder (with residual connections) and a dual-stream, 2D convolutional autoencoder. The publicly available dataset used here contains twelve videos of single home-caged mice alongside frame-level annotations. Under the pretext that the autoencoder only sees normal events, the video data was handcrafted to treat each behaviour as a pseudo-anomaly thereby eliminating them from the others during training. The results are presented for one conspicuous behaviour (hang) and one inconspicuous behaviour (groom). The performance of these models is compared to a single stream autoencoder and a supervised learning model, which are both based on the custom CAE. Both models are also tested on the CUHK Avenue dataset were found to perform as well as some state-of-the-art architectures.
