# SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing

arXiv: https://arxiv.org/abs/2212.04065 · HTML: https://mlsanity.com/paper/2212.04065/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 67 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning](https://mlsanity.com/paper/2109.05687/) (2021)
- [Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning](https://mlsanity.com/paper/1710.04043/) (2017)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [Screen Parsing: Towards Reverse Engineering of UI Models from Screenshots](https://mlsanity.com/paper/2109.08763/) (2021)
- [Visual Interaction with Deep Learning Models through Collaborative Semantic Inference](https://mlsanity.com/paper/1907.10739/) (2019)
- [Interactive Steering of Hierarchical Clustering](https://mlsanity.com/paper/2009.09618/) (2020)
- [SimpModeling: Sketching Implicit Field to Guide Mesh Modeling for 3D Animalmorphic Head Design](https://mlsanity.com/paper/2108.02548/) (2021)
- [The Machine Learning Algorithm as Creative Musical Tool](https://mlsanity.com/paper/1611.00379/) (2016)
- [Evaluation of Interactive Machine Learning Systems](https://mlsanity.com/paper/1801.07964/) (2018)
- [Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation](https://mlsanity.com/paper/1906.07617/) (2019)
- [A Generic Framework and Library for Exploration of Small Multiples through Interactive Piling](https://mlsanity.com/paper/2005.00595/) (2020)
- [Interactive Visualization of Atmospheric Effects for Celestial Bodies](https://mlsanity.com/paper/2010.03534/) (2020)

## Built on by (most cited first, 1 of 1)

- [Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition](https://mlsanity.com/paper/2505.06325/) (2025)

## Abstract (arXiv)

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 textitSpaceEditing to display the visualized data. textitSpaceEditing 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, textitSpaceEditing 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 textitSpaceEditing meets user needs through three case studies while evaluating our proposed new method, and the results confirm the effectiveness of our method.