Unsupervised open-set domain adaptation (UODA) is a realistic problem where unlabeled target data contain unknown classes. Prior methods rely on the coexistence of both source and to perform alignment, which greatly limits their applications when are restricted due privacy concerns. In this paper we address challenging hypothesis transfer setting for UODA, from no longer available during domain...

We present a 2-step optimal transport approach that performs mapping from source distribution to target distribution. Here, the has particularity new classes not in domain. The first step of aims at rejecting samples issued these using an plan. second solves (class ratio) shift still as problem. develop dual solve optimization problem involved each and we prove our results outperform recent sta...

We consider the following problem: Given a set of data and one or more examples of clusters, find a clustering of the whole data set that is consistent with the given clusters. This is essentially a semi-supervised clustering problem, but it differs from previously studied semi-supervised clustering settings in significant ways. Earlier work has shown that none of the existing methods for semi-...

In this article we originate a new class of Grill Set, namely GSβ-Open which is parallel to the β Open Set in Topological Space (X, θ, G). addition, entitle GSβ-continuous and GSβ-open functions by applying review some its important properties. Many examples are given explain concept lucidly. The properties GSβ open sets investigated studied. theorems based on arbitrary union finite intersectio...