Classification for everyone : Building geography agnostic models for fairer recognition

arXiv:2312.02957v3 Announce Type: replace-cross
Abstract: 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 …

abstract analyze art arxiv bias biases building classification cs.ai cs.cv cs.cy cs.lg dataset datasets geography image imagenet images information location paper recognition state state of the art street type