Generalization

We explain how visual attribute learning may benefit from multi-task/transfer learning approaches that carefully select what to share among whom. We show that such methods can combat noisy correlations among attributes, and variations among attribute appearances across object categories.

While learning multiple attributes with possibly noisy correlations in the training set, it helps to employ a multi-task learning approach that tries to learn classifiers that rely on different parts of the input feature space.