# Classification for everyone : Building geography agnostic models for fairer recognition

arXiv: https://arxiv.org/abs/2312.02957 · HTML: https://mlsanity.com/paper/2312.02957/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [Footwear Size Recommendation System](https://mlsanity.com/paper/1806.11423/) (2018)
- [One Embedding To Do Them All](https://mlsanity.com/paper/1906.12120/) (2019)

## Built on by (most cited first, 5 of 5)

- [Shaping Political Discourse using multi-source News Summarization](https://mlsanity.com/paper/2312.11703/) (2023)
- [Exploring Graph Based Approaches for Author Name Disambiguation](https://mlsanity.com/paper/2312.08388/) (2023)
- [Zero-shot Active Learning Using Self Supervised Learning](https://mlsanity.com/paper/2401.01690/) (2024)
- [Detecting anxiety from short clips of free-form speech](https://mlsanity.com/paper/2312.15272/) (2023)
- [Exploring intra-task relations to improve meta-learning algorithms](https://mlsanity.com/paper/2312.16612/) (2023)

## Abstract (arXiv)

In this paper, we analyze different methods to mitigate inherent geographical biases present in state of the art image classification models. We first quantitatively present this bias in two datasets - The Dollar Street Dataset and ImageNet, using images with location information. We then present different methods which can be employed to reduce this bias. Finally, we analyze the effectiveness of the different techniques on making these models more robust to geographical locations of the images.