Classification for everyone : Building geography agnostic models for fairer recognition
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914738275876864 |
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| author | Jindal, Akshat Singh, Shreya Gadgil, Soham |
| author_facet | Jindal, Akshat Singh, Shreya Gadgil, Soham |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02957 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Classification for everyone : Building geography agnostic models for fairer recognition Jindal, Akshat Singh, Shreya Gadgil, Soham Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning 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. |
| title | Classification for everyone : Building geography agnostic models for fairer recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2312.02957 |