# Adjusting for Confounding in Unsupervised Latent Representations of Images

arXiv: https://arxiv.org/abs/1811.06498 · HTML: https://mlsanity.com/paper/1811.06498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

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

- [Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness](https://mlsanity.com/paper/2106.08161/) (2021)

## Abstract (arXiv)

Biological imaging data are often partially confounded or contain unwanted variability. Examples of such phenomena include variable lighting across microscopy image captures, stain intensity variation in histological slides, and batch effects for high throughput drug screening assays. Therefore, to develop "fair" models which generalise well to unseen examples, it is crucial to learn data representations that are insensitive to nuisance factors of variation. In this paper, we present a strategy based on adversarial training, capable of learning unsupervised representations invariant to confounders. As an empirical validation of our method, we use deep convolutional autoencoders to learn unbiased cellular representations from microscopy imaging.