  We propose an interactive editing method that allows humans to help deep
neural networks (DNNs) learn a latent space more consistent with human
knowledge, thereby improving classification accuracy on indistinguishable
ambiguous data. Firstly, we visualize high-dimensional data features through
dimensionality reduction methods and design an interactive system
\textit{SpaceEditing} to display the visualized data. \textit{SpaceEditing}
provides a 2D workspace based on the idea of spatial layout. In this workspace,
the user can move the projection data in it according to the system guidance.
Then, \textit{SpaceEditing} will find the corresponding high-dimensional
features according to the projection data moved by the user, and feed the
high-dimensional features back to the network for retraining, therefore
achieving the purpose of interactively modifying the high-dimensional latent
space for the user. Secondly, to more rationally incorporate human knowledge
into the training process of neural networks, we design a new loss function
that enables the network to learn user-modified information. Finally, We
demonstrate how \textit{SpaceEditing} meets user needs through three case
studies while evaluating our proposed new method, and the results confirm the
effectiveness of our method.
