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nxsEdson / MLCR

Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition (NeurIPS 2019)

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Question About mv-loss

ky941122 opened this issue · comments

commented

Hi, I have recently read your MLCR paper and am very interested in your ideas. But I'm a little confused about the mv-loss.
Why don't you just constrain the features of different views to be mutually orthogonal, but go for the classifier's weights of different views instead?

commented

Sorry for the late reply, and thank you for your interests in our work. There are two reasons for using the weights instead of the features: 1) the features are shared and used for multilabel classification, e.g., 12 AU classification tasks, and 2) the weights for each AU can be regarded as a representation of the features for this specific AU because the weights vector can be seen as the base vector for projection, and the projection results are the final prediction.