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Gradients as Features for Deep Representation Learning
[article]
2020
arXiv
pre-print
We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our key innovation is the design of a linear model that incorporates both gradient and activation of the pre-trained network. We show that our model provides a local linear
arXiv:2004.05529v1
fatcat:gfccfgce2ncodjluczt3crmqh4