Few-shot classification tends to struggle when it needs adapt diverse domains. Due the non-overlapping label space between domains, performance of conventional domain adaptation is limited. Previous work tackles problem in a transductive manner, by assuming access full set test data, which too restrictive for many real-world applications. In this paper, we out tackle issue introducing inductive...

Deep learning (DL)-based techniques have recently proven to be very successful when applied profiled side-channel attacks (SCA). In a real-world SCA scenario, attackers gain knowledge about the target device by getting access similar prior attack. However, most state-of-the-art literature performs only proof-of-concept attacks, where traces intended for profiling and attacking are acquired cons...

We present a simple yet effective unsupervised domain adaptation method that can be generally applied for different NLP tasks. Our method uses unlabeled target domain instances to induce a set of instance similarity features. These features are then combined with the original features to represent labeled source domain instances. Using three NLP tasks, we show that our method consistently outpe...

In this paper, we present a framework for unsupervised domain adaptation of PLDA based i-vector speaker recognition systems. Given an existing out-of-domain PLDA system, we use it to cluster unlabeled in-domain data, and then use this data to adapt the parameters of the PLDA system. We explore two versions of agglomerative hierarchical clustering that use the PLDA system. We also study two auto...

We present a novel approach to perform the unsupervised domain adaptation for object detection through forward-backward cyclic (FBC) training. Recent adversarial training based methods have shown their effectiveness on minimizing discrepancy via marginal feature distributions alignment. However, aligning does not guarantee alignment of class conditional distributions. This limitation is more ev...