Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study. Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research effort has been reported concerning their user evaluation. In this paper, we report on an online betweengroup user study designed to evaluate the performance of saliency maps  a popular explanation algorithm for image classification applications of CNNs. Our results indicate that saliency maps produced by the LRP algorithm helped participants to learn about some specific image features the system is sensitive to. However, the maps seem to provide very limited help for participants to anticipate the networku0027s output for new images. Drawing on our findings, we highlight implications for design and further research on explainable AI. In particular, we argue the HCI and AI communities should look beyond instancelevel explanations.

EMLNET:An Expandable MultiLayer NETwork for Saliency Prediction Abstract   Existing advanced saliency systems have been proposed using Convolutional Neural Networks (CNNs), the prior knowledge of objectness is crucial for saliency detection. However, we show that the use of objectness may also limit the power of CNNs on the images which do not contain a salient object. Besides, one previous study has shown applying a deeper CNN model may not improve the performance, the reason behind could be due to the limited training data. In this work, we aim at investigating the effect of prior knowledge from other domains and proposing a multimodality system. Our work shows the depth of the model still plays an important role in saliency detection when the training data is large enough. To do this in a sophisticated manner can be complex, and also result in unwieldy networks or produce competing objectives that are hard to balance. For the scalability, our multimodality system is trained in an almost endtoend piecewise fashion. The encoder and decoder components are separately trained to deal with complexity tied to the computational paradigm and required space. Therefore, our system can be easily extended to include a variety of prior knowledge for saliency detection. Besides, we also propose a combined saliency loss based on modifications of Pearson correlation and normalized scanpath saliency. Our experiment shows the combined loss can train a CNN model more comprehensively for saliency detection. We denote our expandable multilayer network as EMLNET and our method achieves the stateoftheart results on the public saliency benchmarks, SALICON, MIT300 and CAT2000.