We propose LandLayer , a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit  underlying topological features of the input data structure. We show differentiability with respect to layer inputs, for a general persistent homology with arbitrary filtration. Thus, our proposed layer can be placed anywhere in the network and feed critical information on the topological features of input data into subsequent layers to improve the learnability of the networks toward a given task. A task-optimal structure of LandLayer is learned during training via backpropagation, without requiring any input featurization or data preprocessing. We provide novel stability results, including an adaptation for the robust DTM filtration function , and show that the proposed layer is robust against noise and outliers  . We demonstrate the effectiveness of our approach by classification experiments on various datasets.
We propose PLLay , a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure. In this work, we show differentiability with respect to layer inputs, for a general persistent homology with arbitrary filtration. Thus, our proposed layer can be placed anywhere in the network and feed critical information on the topological features of input data into subsequent layers to improve the learnability of the networks toward a given task. A task-optimal structure of PLLay is learned during training via backpropagation, without requiring any input featurization or data preprocessing. We provide a novel adaptation for the DTM function-based filtration , and show that the proposed layer is robust against noise and outliers through a stability analysis . We demonstrate the effectiveness of our approach by classification experiments on various datasets.
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LandLayer
PLLay
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the
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We
In this work, we
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LandLayer
PLLay
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novel stability results, including an
a novel
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robust DTM filtration function
DTM function-based filtration
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through a stability analysis