Undoing the Damage of Dataset Bias

Aditya Khosla Tinghui Zhou Tomasz Malisiewicz Alexei A. Efros Antonio Torralba


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.


Undoing the Damage of Dataset Bias [paper] [bibtex]
Aditya Khosla, Tinghui Zhou, Tomasz Malisiewicz, Alexei A. Efros, and Antonio Torralba
European Conference on Computer Vision (ECCV), 2012.

Code and Data

Related Papers
Unbiased Look at Dataset Bias, Antonio Torralba and Alexei A. Efros. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011

We thank the anonymous reviewers for their valuable feedback. The paper was co-sponsored by ONR MURIs N000141010933 and N000141010934.

For comments and questions, please contact Aditya Khosla.