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.

翻译：本文分析了缓解当前最优图像分类模型中固有地理偏差的不同方法。我们首先利用带有位置信息的图像，在两个数据集——Dollar Street数据集和ImageNet中——定量展示了这种偏差。随后，我们提出了可用于减少该偏差的多种方法。最后，我们分析了不同技术在提升模型对图像地理位置的鲁棒性方面的有效性。