The success of recent deep convolutional neural networks (CNNs) depends on learning hidden representations that can summarize the important factors of variation behind the data. However, CNNs often criticized as being black boxes that lack interpretability, since they have millions of unexplained model parameters. In this work, we describe Network Dissection, a method that interprets networks by providing labels for the units of their deep visual representations. The proposed method quantifies the interpretability of CNN representations by evaluating the alignment between individual hidden units and a set of visual semantic concepts. By identifying the best alignments, units are given human interpretable labels across a range of objects, parts, scenes, textures, materials, and colors. The method reveals that deep representations are more transparent and interpretable than expected: we find that representations are significantly more interpretable than they would be under a random equivalently powerful basis. We apply the method to interpret and compare the latent representations of various network architectures trained to solve different supervised and self-supervised training tasks. We then examine factors affecting the network interpretability such as the number of the training iterations, regularizations, different initializations, and the network depth and width. Finally we show that the interpreted units can be used to provide explicit explanations of a prediction given by a CNN for an image. Our results highlight that interpretability is an important property of deep neural networks that provides new insights into their hierarchical structure.

本研究介绍了 Network Dissection 方法，通过为深度视觉表示的单元提供标签来解释网络。该方法量化了CNN表示的可解释性，通过评估单个隐藏单元和一组视觉语义概念之间的对齐来识别最佳对齐，认定单位为人类可解释标签。研究发现，深度网络是比预期更加透明和可解释的，并且可用于提供CNN对图像的解释预测，以增强其可解释性，这是深度神经网络的重要属性，提供了对其分层结构的新的洞见。

通过网络剖析解释深度视觉表示