arXiv Open Access 2023

Classification for everyone : Building geography agnostic models for fairer recognition

Akshat Jindal Shreya Singh Soham Gadgil
Lihat Sumber

Abstrak

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.

Penulis (3)

A

Akshat Jindal

S

Shreya Singh

S

Soham Gadgil

Format Sitasi

Jindal, A., Singh, S., Gadgil, S. (2023). Classification for everyone : Building geography agnostic models for fairer recognition. https://arxiv.org/abs/2312.02957

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓