CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Network
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3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition. However, 3D networks can easily lead to over-parameterization which incurs expensive computation cost. In this paper, we propose Channel-wise Automatic KErnel Shrinking (CAKES), to enable efficient 3D learning by shrinking standard 3D convolutions into a set of economic operations (e.g., 1D, 2D convolutions). Unlike previous methods, our proposed CAKES performs channel-wise kernel shrinkage, which enjoys the following benefits: 1) encouraging operations deployed in every layer to be heterogeneous, so that they can extract diverse and complementary information to benefit the learning process; and 2) allowing for an efficient and flexible replacement design, which can be generalized to both spatial-temporal and volumetric data. Together with a neural architecture search framework, by applying CAKES to 3D C2FNAS and ResNet50, we achieve the state-of-the-art performance with much fewer parameters and computational costs on both 3D medical imaging segmentation and video action recognition.
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