Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned mechanisms that contribute to such effective behaviors and more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed "attention masks" support improved interpretability by highlighting which input attributes are critical in determining output. We demonstrate the effectiveness of our framework on a variety of deep neural network architectures in domains from computer vision and natural language processing. The primary contribution of our approach is an interpretable visualization of attention that provides unique insights into the network's underlying decision-making process irrespective of the data modality.
The authors of this paper proposed a data-driven black-box visualization scheme. The paper primarily focuses on neural network models in the experiment section. The proposed method iteratively optimize learnable masks for each training example to find the most relevant content in the input that was "attended" by the neural network.  The authors empirically demonstrated their method on image and text classification tasks. 
Strength:
           - The paper is well-written and easy to follow. 
           - The qualitative analysis of the experimental results nicely illustrated how the learnt latent attention masks match with our intuition about how neural networks make its classification predictions.
        Weakness:
           - Most of the experiments in the paper are performed on small neural networks and simple datesets. I found the method will be more compiling if the authors can show visualization results on ImageNet models. Besides simple object recognition tasks, other more interesting tasks to test out the proposed visualization method are object detection models like end-to-end fast R-CNN, video classification models, and image-captioning models. Overall, the current set of experiments are limited to showcase the effectiveness of the proposed method.
           - It is unclear how the hyperparameter is chosen for the proposed method. How does the \beta affect the visualization quality? It would be great to show a range of samples from high to low beta values. Does it require tuning for different visualization samples? Does it vary over different datasets?