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ODIST: Open world classification via distributionally shifted instances

By Lei Shu, Yassine Benajiba, Saab Mansour, Yi Zhang
2021
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In this work, we address the open-world classification problem with a method called ODIST(open world classification via distributionally shifted instances). This novel and straightforward method can create out-of-domain instances from the in-domain training examples with the help of a pre-trained language model. Experimental results show that ODIST performs better than state-of-the-art decision boundary finding method.
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