ODIST: Open World Classification via Distributionally Shifted Instances
Lei Shu, Yassine Benajiba, Saab Mansour, Yi Zhang · 2021
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-ofdomain 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.