Recently, deep learning has gained exceptional popularity due to its outstanding performances in many machine learning and artificial intelligence applications. Among various deep learning models, convolutional neural network (CNN) is one of the representative models that solved various complex tasks in computer vision since AlexNet, a widely-used CNN model, has won the ImageNet challenge1 in 2012. Even with such a remarkable success, the issue of how it handles the underlying complexity of data so well has not been thoroughly investigated, while much effort was concentrated on pushing its performance to a new limit. Therefore, the current status of its increasing popularity and attention for various applications from both academia and industries is demanding a clearer and more detailed exposition of their inner workings. To this end, we introduce ReVACNN, an interactive visualization system that makes two major contributions: 1) a network visualization module for monitoring the underlying process of a convolutional neural network using a filter-level 2D embedding view and 2) an interactive module that enables real-time steering of a model. We present several use cases demonstrating benefits users can gain from our approach.