Abstract: While deep convolutional neural networks (CNNs) achieve the state-of-the-art performance in visual recognition, they are also criticized as being black boxes that lack interpretability. In this work, we propose a framework called Network Dissection to quantify the interpretability of latent representations of CNNs. By evaluating the alignment between individual hidden units and a set [...]