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Interpretable Convolutional Neural Networks
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This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. In an interpretable CNN, each filter in a high conv-layer represents a certain object part. We do not need any annotations of object parts or textures to supervise the learning process. Instead, the interpretable CNN automatically assigns each filter in a high conv-layer with an object part during the learning process. Our method can be applied to different types of CNNs with different structures. The clear knowledge representation in an interpretable CNN can help people understand the logics inside a CNN, i.e., based on which patterns the CNN makes the decision. Experiments showed that filters in an interpretable CNN were more semantically meaningful than those in traditional CNNs.
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Cited by 1 Pith paper
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Explaining Model Overfitting in CNNs via GMM Clustering
A CNN filter whose feature-map GMM clustering has small outlier clusters is called an anomaly filter and is claimed to indicate model overfitting, but the supporting experiments are weakly consistent.
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