This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, interpretability is always Achilles' heel of deep neural networks. At present, deep neural networks obtain high discrimination power at the cost of a low interpretability of their black-box representations. We believe that high model interpretability may help people break several bottlenecks of deep learning, e.g., learning from a few annotations, learning via human-computer communications at the semantic level, and semantically debugging network representations. We focus on convolutional neural networks (CNNs), and revisit the visualization of CNN representations, methods of diagnosing representations of pre-trained CNNs, approaches for disentangling pre-trained CNN representations, learning of CNNs with disentangled representations, and middle-to-end learning based on model interpretability. Finally, we discuss prospective trends in explainable artificial intelligence. In this paper, we conduct a survey of current studies in understanding neural-network representations and learning neural networks with interpretable/disentangled representations. We can roughly define the scope of the review into the following five research directions:
Zhang et al. REF made a survey for the interpretability of deep visual models.
Visual interpretability for deep learning: a survey
Frontiers of Information Technology & Electronic Engineering
Frontiers of Information Technology & Electronic Engineering
Computer Science