In many real-world problem settings, the training data may contain certain unwanted correlations or biases. Such biases generally occur due to imbalances/skew in the data with respect to some protected attributes. When a standard deep learning model is trained on such biased data, the model will also learn these biases and produce biased predictions. If the available training data is limited, then the impact of biases on the model predictions is magnified, and the performance of existing bias mitigation strategies is also severely affected. We propose a novel approach for mitigating bias in an image classification model at the representation level in the limited data setting. Specifically, we use the protected attribute to guide our representation learning process in the limited data setting such that the learned feature space becomes robust to the biases occurring due to the protected attribute, thereby reducing the impact of bias in the model predictions. We experimentally demonstrate that our proposed bias mitigation approach significantly reduces the model bias score and improves the model performance on seven benchmark datasets. We also demonstrate that our proposed method is also complementary to existing bias mitigation approaches and significantly reduces the bias score of the baseline, sampling, adversarial, domain discriminative, and domain-independent models by 89.97%, 90.94%, 86.74%, 83.62%, and 62.96%, respectively, on the reduced CIFAR-10S dataset. It also significantly improves the mean accuracy of the baseline, sampling, adversarial, domain discriminative, and domain-independent models by absolute margins of 18.83%, 8.32%, 11.78%, 18.74%, and 7.13%, respectively, on reduced CIFAR-10S.