Binarization is a well-known image processing task, whose objective to separate the foreground of an from background. One many tasks for which it useful that preprocessing document images in order identify relevant information, such as text or symbols. The wide variety types, alphabets, and formats makes binarization challenging. There are multiple proposals with solve this problem, classical m...

Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in target domain where labeled data are not available by leveraging information from annotated source domain. Most deep UDA approaches operate single-source, single-target scenario, i.e., they assume that and samples arise single distribution. However, practice most datasets can be regarded as mixtures multiple domai...

Domain adaptation aims to leverage a labeled source domain learn classifier for the unlabeled target with different distribution. Previous methods mostly match distribution between two domains by global or class alignment. However, alignment cannot achieve fine-grained class-to-class overlap; supervised pseudo-labels guarantee their reliability. In this paper, we propose simple yet efficient me...

We consider unsupervised domain adaptation (UDA) for classification problems in the presence of missing data unlabelled target domain. More precisely, motivated by practical applications, we analyze situations where distribution shift exists between domains and some components are systematically absent on without available supervision imputing components. propose a generative approach imputatio...

To cope with machine learning problems where the learner receives data from different source and target distributions, a new learning framework named domain adaptation (DA) has emerged, opening the door for designing theoretically well-founded algorithms. In this paper, we present SLDAB, a self-labeling DA algorithm, which takes its origin from both the theory of boosting and the theory of DA. ...

Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on lar...

In Statistical Machine Translation, in-domain and out-of-domain training data are not always clearly delineated. This paper investigates how we can still use mixture-modeling techniques for domain adaptation in such cases. We apply unsupervised clustering methods to split the original training set, and then use mixture-modeling techniques to build a model adapted to a given target domain. We sh...

Recent studies have shown that pseudo labels can contribute to unsupervised domain adaptation (UDA) for speaker verification. Inspired by the self-training strategies use an existing classifier label unlabeled data retraining, we propose a cluster-guided UDA framework target clustering and combines labeled source pseudo-labeled train embedding network. To improve cluster quality, network dedica...

Crime risk prediction is crucial for city safety and residents’ life quality. However, without labeled data, it challenging to predict crime in cities. Due municipal regulations maintenance costs, not trivial many cities collect high-quality data. In particular, some have lots of data while others may few. It has been possible develop a model by learning knowledge from with abundant Nevertheles...

This letter addresses the problem of domain adaptation for task music source separation. Using datasets from two different domains, we compare performance a deep learning-based harmonic-percussive separation model under training scenarios, including supervised joint using data both domains and pre-training in one with fine-tuning another. We propose an adversarial unsupervised approach suitable...