Adversarial Concept Erasure in Kernel Space. (arXiv:2201.12191v1 [cs.LG])

Web: http://arxiv.org/abs/2201.12191

The representation space of neural models for textual data emerges in an
unsupervised manner during training. Understanding how human-interpretable
concepts, such as gender, are encoded in these representations would improve
the ability of users to \emph{control} the content of these representations and
analyze the working of the models that rely on them. One prominent approach to
the control problem is the identification and removal of linear concept
subspaces -- subspaces in the representation space that correspond to a given
concept. …

arxiv kernel space