Local Relation Networks for Image Recognition
For an effective compo-sition of features, suitable filters would need to be learned and applied. This requirement is problematic when trying to infer visual concepts that have significant spatial vari-ability, such as from geometric deformation as illustrated in
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What Have We Learned From Deep Representations for …
openaccess.thecvf.comwhat these powerful models actually have learned. In this paper we shed light on deep spatiotemporal net-works by visualizing what excites the learned models us-ing activation maximization by backpropagating on the in-put. We are the first to visualize the hierarchical features
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