Controllable Invariance through Adversarial Feature Learning
Table 2: Results on Extended Yale B dataset. A better representation has lower accuracy of classifying factor s and higher accuracy of classifying label y
['Method', 'Accuracy of classifying  [ITALIC] s', 'Accuracy of classifying  [ITALIC] y']
The advantage of factoring out lighting conditions is shown by the improved accuracy 89% for classifying identities, while the best baseline achieves an accuracy of 85%. We see that, without removing lighting conditions, the images are clustered based on the lighting conditions. After removing information of lighting conditions, images are clustered according to the identity of each person.