Understanding the Role of Individual Units in a Deep Neural Network
This work addresses the interpretability challenge in deep learning for researchers and practitioners, offering a method to understand and control network behavior, though it is incremental in building on existing analytic techniques.
The authors tackled the problem of interpreting learned representations in deep neural networks by introducing network dissection, a framework that identifies the semantics of individual units in image classification and generation networks, revealing that units correspond to object concepts and can be manipulated for tasks like adversarial attack analysis and image editing.
Deep neural networks excel at finding hierarchical representations that solve complex tasks over large data sets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing.