Fig. 1. ACTIVIS integrates several coordinated views to support exploration of complex deep neural network models, at both instanceand subset-level. 1. Our user Susan starts exploring the model architecture, through its computation graph overview (at A). Selecting a data node (in yellow) displays its neuron activations (at B). 2. The neuron activation matrix view shows the activations for instances and instance subsets; the projected view displays the 2-D projection of instance activations. 3. From the instance selection panel (at C), she explores individual instances and their classification results. 4. Adding instances to the matrix view enables comparison of activation patterns across instances, subsets, and classes, revealing causes for misclassification.Abstract-While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ACTIVIS, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance-and subset-level. ACTIVIS has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ACTIVIS may work with different models.

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The Object-Subject-Predicate triple annotation system is now well adopted in the research community, however, it does not always speak to end-users. In fact, explaining all the complexity of semantic annotation systems to laymen can sometime be difficult. We believe that this communication can be simplified by providing a meaningful abstraction of the state of the art in semantic annotation models and thus, in this article, we describe the issue of semantic annotation and review a number of research and end-user tools in the field. Doing so, we provide a clear classification schemes of the features of annotation systems. We then show how this scheme can be used to clarify requirements of end-user use cases and thus simplify the communication between semantic annotation experts and the actual users of this technology.

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