Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming. (arXiv:2206.10816v1 [cs.LG])

Across applications spanning supervised classification and sequential
control, deep learning has been reported to find "shortcut" solutions that fail
catastrophically under minor changes in the data distribution. In this paper,
we show empirically that DNNs can be coaxed to avoid poor shortcuts by
providing an additional "priming" feature computed from key input features,
usually a coarse output estimate. Priming relies on approximate domain
knowledge of these task-relevant key input features, which is often easy to
obtain in practical settings. For …

arxiv dnn lg