You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: The first tool is software that interactively plots the activations produced on each layer of a trained DNN for user-provided images or video. Static images afford a slow, detailed investigation of a particular input, whereas video input highlights the DNNs changing responses to dynamic input. At present, the videos are processed live from a user’s computer camera, which is especially helpful because users can move different items around the field of view, occlude and combine them, and perform other manipulations to actively learn how different features in the network respond. The second tool — new regularizations that enable improved, interpretable, optimized visualizations of learned features — will help researchers and practitioners understand, debug, and improve their models. The visualizations also reveal a new twist in an ongoing story. Previous studieshave shown that discriminative networks can easily be fooled or hacked by the addition of certain structurednoise in image space (Szegedy et al., 2013; Nguyen et al., 2014).An oft-cited reason for this property is that discriminative training leads networksto ignore non-discriminative information in their input, e.g. learning to detect jaguars by matching the unique spots on their fur while ignoring the fact that they have four legs. For this reason it has been seen as a hopeless endeavor to create a generative model in which one randomly samples an x from a broad distribution on the space of all possible imagesand then iteratively transforms x into a recognizable image by moving it to a region that satisfies both a prior p(x) and posterior p(y|x) for some class label y.Past attempts have largely supported this view by producing unrealistic images using this method (Nguyen et al., 2014; Simonyan et al., 2013).

Question: What are the examples of the tools that enable understanding of Neural Networks for newcomers in deep learning?
The paper talked about two main tools; the first is a software tool to plot activations of each trained layer of a network, for images or videos. Second is introducing new regularization ways to help with understanding learned features through network.These tools are supposed to help newcomers in deep learning to have better intuitions for hidden interpretations of well known structures and give motivations for more new ideas.