This article takes a problem-oriented perspective and presents a comprehensive review of
transfer-learning methods, both shallow and deep, for cross-dataset visual recognition …

Abstract Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an
unlabeled dataset by transferring knowledge from a labeled source data, which has been …

Cervical cancer remains a significant cause of mortality in low-income countries. As in many
other diseases, the existence of several screening/diagnosis methods and subjective …

All famous machine learning algorithms that comprise both supervised and semi-supervised
learning work well only under a common assumption: the training and test data follow the …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Applying a trained model on a new scenario may suffer from domain shift. Unsupervised
domain adaptation (UDA) has been proven to be an effective approach to solve the problem …

Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer
learning, with a particular focus placed on domain adaptation from a theoretical point-of …

We establish a data-dependent notion of algorithmic stability for Stochastic Gradient
Descent (SGD), and employ it to develop novel generalization bounds. This is in contrast to …

In this paper we make the first attempt at achieving the cross-modal (ie image-to-events)
adaptation for event-based object recognition without accessing any labeled source image …

In this survey, we propose to explore and discuss the common rules behind knowledge
transfer works for vision recognition tasks. To achieve this, we firstly discuss the different …