ODIST: Open world classification via distributionally shifted instances
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-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.
Research areas