Autism is a neurodevelopmental disorder characterized by deficits in social, interpersonal interaction and communication skills. A generalized facial emotion recognition model does not scale well when confronted with the emotions of autistic children due to domain shift inherent distributions source (neurotypical) target (autistic) population. The dearth labeled datasets field autism exacerbate...

When can we expect a text-video retrieval system to work effectively on datasets that differ from its training domain? In this work, investigate question through the lens of unsupervised domain adaptation in which objective is match natural language queries and video content presence shift at query-time. Such systems have significant practical applications since they are capable generalising ne...

While deep learning has led to significant advances in visual recognition over the past few years, such advances often require a lot of annotated data. While unsupervised domain adaptation has emerged as an alternative approach that doesn’t require as much annotated data, prior evaluations of domain adaptation have been limited to relatively simple datasets. This work pushes the state of the ar...

The main goal of this work is the adaptation of a broadcast news transcription system to a new domain, namely, the Portuguese Parliament plenary meetings. This paper describes the different domain adaptation steps that lowered our baseline absolute word error rate from 20.1% to 16.1%. These steps include the vocabulary selection, in order to include specific domain terms, language model adaptat...

Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised domain adaptation. The NET reduces cross-domain disparity through nonlinear domain alignment. It also embeds the domainaligned data such that similar data points ...

This paper investigates supervised and unsupervised adaptation of stochastic grammars, including ngram language models and probabilistic context-free grammars (PCFGs), to a new domain. It is shown that the commonly used approaches of count merging and model interpolation are special cases of a more general maximum a posteriori (MAP) framework, which additionally allows for alternate adaptation ...

• A novel idea of exploring instance-wise localised source knowledge for unsupervised cross-domain person re-id. Hierarchical Unsupervised Domain Adaptation method designed to discover at the instance level. Analyse feature representations domain adaptation in closed-set supervised learning vs. open-set learning. Most existing re-identification (re-id) methods assume model training on a separat...

In this chapter, we present CORrelation ALignment (CORAL), a simple yet effective method for unsupervised domain adaptation. CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather th...

We propose a novel method for unsupervised domain adaptation. Traditional machine learning algorithms often fail to generalize to new input distributions, causing reduced accuracy. Domain adaptation attempts to compensate for the performance degradation by transferring and adapting source knowledge to target domain. Existing unsupervised methods project domains into a lower-dimensional space an...

The success of deep learning in computer vision is mainly attributed to an abundance of data. However, collecting large-scale data is not always possible, especially for the supervised labels. Unsupervised domain adaptation (UDA) aims to utilize labeled data from a source domain to learn a model that generalizes to a target domain of unlabeled data. A large amount of existing work uses Siamese ...