Unsupervised Domain Adaptation using Graph Transduction Games

Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper, we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a fully automatized iterative algorithm for UDA based on graph transduction games (GTG). The main advantages of this approach are its principled foundation, guaranteed termination of the iterative algorithms to a Nash equilibrium (which corresponds to a consistent labeling condition) and soft labels quantifying the uncertainty of the label assignment process. We also investigate the beneficial effect of using pseudo-labels from linear classifiers to initialize the iterative process. The performance of the resulting methods is assessed on publicly available object recognition benchmark datasets involving both shallow and deep features. Results of experiments demonstrate the suitability of the proposed game-theoretic approach for solving UDA tasks.Comment: Oral IJCNN 201

Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly

Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.Comment: Accepted by TPAMI in July, 2018. We introduce Proposed Split Version 2.0 (Please download it from our project webpage). arXiv admin note: substantial text overlap with arXiv:1703.0439

A transfer learning approach to drug resistance classification in mixed HIV dataset

Funding: This research is funded by the Tertiary Education Trust Fund (TETFund), Nigeria.As we advance towards individualized therapy, the ‘one-size-fits-all’ regimen is gradually paving the way for adaptive techniques that address the complexities of failed treatments. Treatment failure is associated with factors such as poor drug adherence, adverse side effect/reaction, co-infection, lack of follow-up, drug-drug interaction and more. This paper implements a transfer learning approach that classifies patients' response to failed treatments due to adverse drug reactions. The research is motivated by the need for early detection of patients' response to treatments and the generation of domain-specific datasets to balance under-represented classification data, typical of low-income countries located in Sub-Saharan Africa. A soft computing model was pre-trained to cluster CD4+ counts and viral loads of treatment change episodes (TCEs) processed from two disparate sources: the Stanford HIV drug resistant database (https://hivdb.stanford.edu), or control dataset, and locally sourced patients' records from selected health centers in Akwa Ibom State, Nigeria, or mixed dataset. Both datasets were experimented on a traditional 2-layer neural network (NN) and a 5-layer deep neural network (DNN), with odd dropout neurons distribution resulting in the following configurations: NN (Parienti et al., 2004) [32], NN (Deniz et al., 2018) [53] and DNN [9 7 5 3 1]. To discern knowledge of failed treatment, DNN1 [9 7 5 3 1] and DNN2 [9 7 5 3 1] were introduced to model both datasets and only TCEs of patients at risk of drug resistance, respectively. Classification results revealed fewer misclassifications, with the DNN architecture yielding best performance measures. However, the transfer learning approach with DNN2 [9 7 3 1] configuration produced superior classification results when compared to other variants/configurations, with classification accuracy of 99.40%, and RMSE values of 0.0056, 0.0510, and 0.0362, for test, train, and overall datasets, respectively. The proposed system therefore indicates good generalization and is vital as decision-making support to clinicians/physicians for predicting patients at risk of adverse drug reactions. Although imbalanced features classification is typical of disease problems and diminishes dependence on classification accuracy, the proposed system still compared favorably with the literature and can be hybridized to improve its precision and recall rates.Publisher PDFPeer reviewe

Estimating self-assessed personality from body movements and proximity in crowded mingling scenarios

ArtículoThis paper focuses on the automatic classi cation of self- assessed personality traits from the HEXACO inventory du- ring crowded mingle scenarios. We exploit acceleration and proximity data from a wearable device hung around the neck. Unlike most state-of-the-art studies, addressing per- sonality estimation during mingle scenarios provides a cha- llenging social context as people interact dynamically and freely in a face-to-face setting. While many former studies use audio to extract speech-related features, we present a novel method of extracting an individual's speaking status from a single body worn triaxial accelerometer which scales easily to large populations. Moreover, by fusing both speech and movement energy related cues from just acceleration, our experimental results show improvements on the estima- tion of Humility over features extracted from a single behav- ioral modality. We validated our method on 71 participants where we obtained an accuracy of 69% for Honesty, Consci- entiousness and Openness to Experience. To our knowledge, this is the largest validation of personality estimation carried out in such a social context with simple wearable sensors