Deep Learning Development Environment in Virtual Reality
This work addresses the problem of deep learning accessibility for biomedical professionals by providing an immersive, interactive tool, though it is incremental as it applies existing VR techniques to a new domain.
The paper tackles the challenge of making deep learning model development more accessible and intuitive by creating a virtual reality environment where users can physically construct neural networks with their hands, automatically translating configurations into trainable models and reporting test accuracy in real-time.
Virtual reality (VR) offers immersive visualization and intuitive interaction. We leverage VR to enable any biomedical professional to deploy a deep learning (DL) model for image classification. While DL models can be powerful tools for data analysis, they are also challenging to understand and develop. To make deep learning more accessible and intuitive, we have built a virtual reality-based DL development environment. Within our environment, the user can move tangible objects to construct a neural network only using their hands. Our software automatically translates these configurations into a trainable model and then reports its resulting accuracy on a test dataset in real-time. Furthermore, we have enriched the virtual objects with visualizations of the model's components such that users can achieve insight about the DL models that they are developing. With this approach, we bridge the gap between professionals in different fields of expertise while offering a novel perspective for model analysis and data interaction. We further suggest that techniques of development and visualization in deep learning can benefit by integrating virtual reality.