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We explore multi-scale convolutional neural nets (CNNs) for image classification. Contemporary approaches extract features from a single output layer. By extracting features from multiple layers, one can simultaneously reason about high, mid, and low-level features during classification. The resulting multi-scale architecture can itself be seen as a feed-forward model that is structured as a directed acyclic graph (DAG-CNNs). We use DAG-CNNs to learn a set of multiscale features that can bedoi:10.1109/iccv.2015.144 dblp:conf/iccv/YangR15 fatcat:yixsrpsdtfhglbv5p3ehw7lthq