# Semi-Automatic Data Annotation guided by Feature Space Projection

arXiv: https://arxiv.org/abs/2007.13689 · HTML: https://mlsanity.com/paper/2007.13689/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 11 of 11)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotation](https://mlsanity.com/paper/2502.15980/) (2025)
- [Iterative Pseudo-Labeling with Deep Feature Annotation and Confidence-Based Sampling](https://mlsanity.com/paper/2109.02717/) (2021)
- [INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation](https://mlsanity.com/paper/2208.04104/) (2022)
- [Rethinking Interactive Image Segmentation: Feature Space Annotation](https://mlsanity.com/paper/2101.04378/) (2021)
- [Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction](https://mlsanity.com/paper/2110.00317/) (2021)
- [Granular-ball Representation Learning for Deep CNN on Learning with Label Noise](https://mlsanity.com/paper/2409.03254/) (2024)
- [Performance of Human Annotators in Object Detection and Segmentation of Remotely Sensed Data](https://mlsanity.com/paper/2409.10272/) (2024)
- [Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning](https://mlsanity.com/paper/2302.02663/) (2023)
- [Co-Training with Active Contrastive Learning and Meta-Pseudo-Labeling on 2D Projections for Deep Semi-Supervised Learning](https://mlsanity.com/paper/2504.18666/) (2025)
- [ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation](https://mlsanity.com/paper/2608.05026/) (2026)

## Abstract (arXiv)

Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.