SpaceEditing: A Latent Space Editing Interface for Integrating Human Knowledge into Deep Neural Networks

Jiafu Wei, Ding Xia, Haoran Xie, Chia-Ming Chang, Chuntao Li, Xi Yang

Abstract: Human-centered AI aims to bridge the gap between machine decision-making and human understanding. However, even for classification tasks where deep neural networks have achieved superb performance, there are currently few methods that link humans and AI well, especially on domain-specific tasks. In this paper, we propose SpaceEditing, a 2D spatial layout tool that enables human users to interact with the latent space of deep neural networks. During the interaction process, the tool’s algorithm automatically processes user actions, providing feedback to the network and leveraging triplet loss to effectively learn from user-modified information. We evaluate SpaceEditing with three case studies: (1) an archaeology researcher uses a bronze dataset; (2) a deep learning researcher uses a garbage classification dataset; (3) six deep learning beginners use a head pose dataset. The experimental results demonstrate the effectiveness of our tool in integrating human knowledge and improving network performance.