# InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity

arXiv: https://arxiv.org/abs/1712.00193 · HTML: https://mlsanity.com/paper/1712.00193/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 8 of 8)

- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Bias Mimicking: A Simple Sampling Approach for Bias Mitigation](https://mlsanity.com/paper/2209.15605/) (2022)
- [The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification](https://mlsanity.com/paper/2105.02778/) (2021)
- [From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition](https://mlsanity.com/paper/2308.04553/) (2023)
- [Fair Visual Recognition in Limited Data Regime using Self-Supervision and Self-Distillation](https://mlsanity.com/paper/2107.00067/) (2021)
- [Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations](https://mlsanity.com/paper/2201.03045/) (2022)
- [How Technology Impacts and Compares to Humans in Socially Consequential Arenas](https://mlsanity.com/paper/2211.03554/) (2022)
- [Mitigating Bias in Concept Bottleneck Models for Fair and Interpretable Image Classification](https://mlsanity.com/paper/2603.05899/) (2026)

## Abstract (arXiv)

We demonstrate an approach to face attribute detection that retains or improves attribute detection accuracy across gender and race subgroups by learning demographic information prior to learning the attribute detection task. The system, which we call InclusiveFaceNet, detects face attributes by transferring race and gender representations learned from a held-out dataset of public race and gender identities. Leveraging learned demographic representations while withholding demographic inference from the downstream face attribute detection task preserves potential users' demographic privacy while resulting in some of the best reported numbers to date on attribute detection in the Faces of the World and CelebA datasets.