@article{Kundu_Patel_Srivastava_Chaube_Moorthy_2022, title={Handling Colors in Image Classification}, volume={7}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/4889}, DOI={10.15353/jcvis.v7i1.4889}, abstractNote={<p>The presence of bias in a dataset has been a long standing bottleneck in the task of image classification. While supervised methods have shown to overcome these biases, self-supervised methods have managed to overcome benchmarks set by supervised learning methods. This paper shows that self-supervised methods can maintain their ability to outperform supervised methods even when introduced to color bias. Two experimentation pipelines are presented. One focuses on the capability of a model to handle artificially induced color bias and the other gauges the ability of a model to incorporate naturally occurring color differences present in vision datasets.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Kundu, Soumya S and Patel, Sheel and Srivastava, Shaswat and Chaube, Aarsh and Moorthy, Vaishnavi}, year={2022}, month={Apr.}, pages={22–24} }