The preparation of a large and diverse dataset is essential for training a robust deep learning model. However, there are instances where certain data are theoretically possible but challenging to observe in reality (e.g., traffic light violations). We refer to these unique instances as 'Scarce Realities', highlighting their rarity and the difficulties they present in data collection and model training. One effective and emerging approach involves using generative models to generate and augment such data. In this study, we demonstrate the promising potential of combining object detection models with simple image generation models as a way to generate fake videos. We achieve this by partially editing existing videos to artificially create 'Scarce Realities', using the generation of fake dashboard camera footage of traffic light violations as an example.

Deep learning models need a lot of labeled data, which is costly and time-consuming to collect. A method that learns a common knowledge (meta-knowledge) from similar tasks to train new tasks efficiently with fewer data is effective. We propose "Feature-adaptive Re-MAML" (FARe-MAML), a novel method for acquiring the most optimal learning method, taking into account the features of the new training data.

Data binding in front-end user interface web development allows for the UI to update automatically in sync with data, removing complexity from development and simplifying programming. However, data binding often causes website performance to degrade due to its increased complexity. In this paper, we propose "Visible Anchors" to solve the performance degradation caused by data binding. We present a novel web front-end compiler called FaST that builds upon this idea. We also compare the rendering speed of websites built by existing methods and the FaST compiler. The evaluation result revealed that the websites built by the FaST compiler are rendered at a minimum 17.1 times faster than the ones built by the existing methods. Most notably, FaST achieves all of this without any additional cost to the developer.