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Undoing the Damage of Dataset Bias

Author(s)
Khosla, Aditya
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
The presence of bias in existing object recognition datasets is now well-known in the computer vision community. While it remains in question whether creating an unbiased dataset is possible given limited resources, in this work we propose a discriminative framework that directly exploits dataset bias during training. In particular, our model learns two sets of weights: (1) bias vectors associated with each individual dataset, and (2) visual world weights that are common to all datasets, which are learned by undoing the associated bias from each dataset. The visual world weights are expected to be our best possible approximation to the object model trained on an unbiased dataset, and thus tend to have good generalization ability. We demonstrate the effectiveness of our model by applying the learned weights to a novel, unseen dataset, and report superior results for both classification and detection tasks compared to a classical SVM that does not account for the presence of bias. Overall, we find that it is beneficial to explicitly account for bias when combining multiple datasets. Keywords: Target Domain; Domain Adaptation; Transfer Learning; Visual World; Spatial Pyramid
Date issued
2012-09
URI
https://hdl.handle.net/1721.1/122669
Department
Massachusetts Institute of Technology. Media Laboratory
Journal
European Conference on Computer Vision
Publisher
Springer Nature
Citation
Kholsa, Aditya et al. "Undoing the Damage of Dataset Bias." European Conference on Computer Vision, September 2012, Munich, Germany, Springer Nature, 2012 © 2012 Springer-Verlag
Version: Author's final manuscript
ISBN
978-3-642-33717-8
978-3-642-33718-5

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