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GUI animations, such as card movement, menu slide in/out, snackbar display, provide appealing user experience and enhance the usability of mobile applications. These GUI animations should not violate the platform's UI design guidelines (referred to as designdon't guideline in this work) regarding component motion and interaction, content appearing and disappearing, and elevation and shadow changes. However, none of existing static code analysis, functional GUI testing and GUI image comparison techniques can "see" the GUI animations on the scree, and thus they cannot support the linting of GUI animations against design-don't guidelines. In this work, we formulate this GUI animation linting problem as a multi-class screencast classification task, but we do not have suf- ficient labeled GUI animations to train the classifier. Instead, we propose an unsupervised, computer-vision based adversarial autoencoder to solve this linting problem. Our autoencoder learns to group similar GUI animations by "seeing" lots of unlabeled realapplication GUI animations and learning to generate them. As the first work of its kind, we build the datasets of synthetic and realworld GUI animations. Through experiments on these datasets, we systematically investigate the learning capability of our model and its effectiveness and practicality for linting GUI animations, and identify the challenges in this linting problem for future work [ABSTRACT FROM AUTHOR]