Deep learning is ubiquitous, but its lack of transparency limits its impact on several potential application areas. We demonstrate a virtual reality tool for automating the process of assigning data inputs to different categories. A dataset is represented as a cloud of points in virtual space. The user explores the cloud through movement and uses hand gestures to categorise portions of the cloud. This triggers gradual movements in the cloud: points of the same category are attracted to each other, different groups are pushed apart, while points are globally distributed in a way that utilises the entire space. The space, time, and forces observed in virtual reality can be mapped to well-defined machine learning concepts, namely the latent space, the training epochs and the backpropagation. Our tool illustrates how the inner workings of deep neural networks can be made tangible and transparent. We expect this approach to accelerate the autonomous development of deep learning applications by end users in novel areas.

翻译：深度学习无处不在，但其缺乏透明度限制了其在多个潜在应用领域的影响力。我们展示了一种虚拟现实工具，用于自动化将数据输入分配到不同类别的过程。数据集在虚拟空间中被表示为点云。用户通过移动探索点云，并使用手势对点云的特定部分进行分类。这会触发点云的渐进式运动：相同类别的点相互吸引，不同组群被推开，同时点在整个空间中全局分布以充分利用空间。虚拟现实中观察到的空间、时间与力可映射到明确的机器学习概念，即潜在空间、训练周期和反向传播。我们的工具说明了深度神经网络的内在工作机制如何变得可触知且透明。我们预期这种方法将加速最终用户在新兴领域中自主开发深度学习应用的进程。