Learning with Unavailable Data: Generalized and Open Zero-Shot Learning

The field of visual object recognition has seen a significant progress in recent years thanks to the availability of large-scale annotated datasets. However, labelling a large amount of data is difficult and costly and can be simply infeasible for some classes due to the long-tail instances distribution problem. Zero-Shot Learning (ZSL) is a framework that consider the case in which for some of the classes no labeled training examples are available to train the model. To solve the problem a multi-modal source of information, the class (semantic) embeddings, is exploited to extract knowledge from the available classes, the seen classes, and recognize novel categories for which the class embeddings is the only information available, namely, the unseen classes. To directly targeting the extreme imbalance in the data, in this thesis, we first propose a methodology to improve synthetic data generation for the unseen classes through their class embeddings. Second, we propose to generalize the Zero-Shot Learning framework towards a more competitive and real-world oriented scenario. Thus, we formalize the problem of Open Zero-Shot Learning as the problem of recognizing seen and unseen classes, as in ZSL, while also rejecting instances from unknown categories, for which neither visual data nor class embeddings are provided. Finally, we propose methodologies to not only generate unseen categories, but also the unknown ones

Hormone therapy after the Women's Health Initiative: a qualitative study

BACKGROUND: Publication of results from the Women's Health Initiative study in July 2002 was a landmark event in biomedical science related to postmenopausal women. The purpose of this study was to describe the impact of new hormone therapy recommendations on patients' attitudes and decision-making in a primary care practice. METHODS: A questionnaire including structured and open-ended questions was administered in a family practice office waiting room from August through October 2003. Rationale for taking or not taking hormone therapy was specifically sought. Women 50–70 years old attending for office visits were invited to participate. Data were analyzed qualitatively and with descriptive statistics. Chart review provided medication use rates for the entire practice cohort of which the sample was a subset. RESULTS: Respondents (n = 127) were predominantly white and well educated, and were taking hormone therapy at a higher rate (38%) than the overall rate (26%) for women of the same age range in this practice. Belief patterns about hormone therapy were, in order of frequency, 'use is risky', 'vindication or prior beliefs', 'benefit to me outweighs risk', and 'unaware of new recommendations'. Twenty-eight out of 78 women continued hormones use after July 2002. Of 50 women who initially stopped hormone therapy after July 2002, 12 resumed use. Women who had stopped hormone therapy were a highly symptomatic group. Responses with emotional overtones such as worry, confusion, anger, and grief were common. CONCLUSION: Strategies for decision support about hormone therapy should explicitly take into account women's preferences about symptom relief and the trade-offs among relevant risks. Some women may need emotional support during transitions in hormone therapy use

Decision-making about the use of hormone therapy among perimenopausal women

Women reaching menopause must make a controversial decision about whether to use hormone therapy (HT). The theory of planned behaviour (TPB) was the organizing framework. The objectives were to determine if (1) influence of different TPB constructs varied with stage of menopause and HT use, (2) women with diabetes were influenced in significantly different ways from women without, (3) the overall perceived behavioural control (PBC) and self-efficacy (SE) have independent effects on intention, and (4) physician influence was mediated by subjective norm (SN).Cross-sectional survey of women from a managed care organization.Multiple regression analysis was used to analyse 765 responses (230 from women with diabetes) and separately four main subgroups: (1) early menopause stage and never used HT, (2) late menopause stage and never used HT, (3) late menopause stage and previously used HT, and (4) late menopause stage currently using HT.For the entire sample, the model explains 68% of variance in intention, where SE, physicians' influence, self-identification with menopause as a natural part of ageing, self-identification as someone who wants to delay menopause, HT status, menopause status, and diabetes were added to the TPB. For the entire sample, SE added 2% to the explained variance and the physician determinant added 7%.An augmented TPB is useful for understanding women's HT use decisions. The theory explains more variance in intention before a behaviour is enacted than after, and decision structure changes over time. PBC and SE have independent effects on intention.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79377/1/135910709X457946.pd

Transductive Zero-Shot Learning by Decoupled Feature Generation

In this paper, we address zero-shot learning (ZSL), the problem of recognizing categories for which no labeled visual data are available during training. We focus on the transductive setting, in which unlabelled visual data from unseen classes is available. State-of-the-art paradigms in ZSL typically exploit generative adversarial networks to synthesize visual features from semantic attributes. We posit that the main limitation of these approaches is to adopt a single model to face two problems: 1) generating realistic visual features, and 2) translating semantic attributes into visual cues. Differently, we propose to decouple such tasks, solving them separately. In particular, we train an unconditional generator to solely capture the complexity of the distribution of visual data and we subsequently pair it with a conditional generator devoted to enrich the prior knowledge of the data distribution with the semantic content of the class embeddings. We present a detailed ablation study to dissect the effect of our proposed decoupling approach, while demonstrating its superiority over the related stateof-the-art

Towards Open Zero-Shot Learning

In Generalized Zero-Shot Learning (GZSL), unseen categories (for which no visual data are available at training time) can be predicted by leveraging their class embeddings (e.g., a list of attributes describing them) together with a complementary pool of seen classes (paired with both visual data and class embeddings). Despite GZSL is arguably challenging, we posit that knowing in advance the class embeddings, especially for unseen categories, is an actual limit of the applicability of GZSL towards real-world scenarios. To relax this assumption, we propose Open Zero-Shot Learning (OZSL) as the problem of recognizing seen and unseen classes (as in GZSL) while also rejecting instances from unknown categories, for which neither visual data nor class embeddings are provided. We formalize the OZSL problem introducing evaluation protocols, error metrics and benchmark datasets. We also tackle the OZSL problem by proposing and evaluating the idea of performing unknown feature generation