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Found 3 papers, 1 papers with code
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available.
Selective Forgetting of Deep Networks at a Finer Level than Samples
Such forgetting is crucial also in a practical sense since the deployed DNNs may be trained on the data with outliers, poisoned by attackers, or with leaked/sensitive information.
Multi Instance Learning For Unbalanced Data
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective.
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