Hierarchical Subquery Evaluation for Active Learning on a Graph

To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between runs on the same dataset. We propose perplexity based graph construction and a new hierarchical subquery evaluation algorithm to combat this variability, and to release the potential of Expected Error Reduction. Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning. Until now, it has also been prohibitively costly to compute for sizeable datasets. We demonstrate our highly practical algorithm, comparing it to other active learning measures on classification datasets that vary in sparsity, dimensionality, and size. Our algorithm is consistent over multiple runs and achieves high accuracy, while querying the human expert for labels at a frequency that matches their desired time budget.Comment: CVPR 201

A qualitative exploratory study: Using medical students’ experiences to review the role of a rural clinical attachment in KwaZulu-Natal

Objectives: There are challenges when it is considered that a main role of a rural clinical attachment for medical students is to encourage students to return after graduation to practise in rural areas. This view may lead to the relative neglect of other potential valuable roles with regard to rural  exposure. This paper draws on the Force Field Model of teacher development to describe medical students’ experiences, illustrate the complexity of interacting factors during rural exposure, caution that experiences cannot be predicted and highlight the positive incentives of a rural clinical attachment.Design: The design was explorative, descriptive and qualitative.Setting: The study setting was a district hospital in rural KwaZulu-Natal.Subjects: The participants were four final-year medical students who had completed a compulsory attachment during their Family Medicine rotation.Outcome measures: Data were collected using photo elicitation and analysed using the Force Field Model.Results: The participants felt that overall the experience was positive. The effect of biography and contextual forces were not as strong as expected. Institutional forces were important and programmatic forces tended to have a negative effect on experiences. The participants particularly enjoyed being acknowledged and felt empathy for the difficult tasks of doctors.Conclusion: The potential role of a clinical attachment may go beyond attracting students to practise in rural areas. The experience can be beneficial, irrespective of where the student decides to practise after graduation. There is a need for a review of the rural attachment curriculum and paedagogy. Caution should be used when screening medical students for suitability to work in rural areas prior to rural exposure.Keywords: rural clinical attachment, family medicine rotation, medical students, experiences, Force Field Mode

Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models

Out-of-Distribution detection between dataset pairs has been extensively explored with generative models. We show that likelihood-based Out-of-Distribution detection can be extended to diffusion models by leveraging the fact that they, like other likelihood-based generative models, are dramatically affected by the input sample complexity. Currently, all Out-of-Distribution detection methods with Diffusion Models are reconstruction-based. We propose a new likelihood ratio for Out-of-Distribution detection with Deep Denoising Diffusion Models, which we call the Complexity Corrected Likelihood Ratio. Our likelihood ratio is constructed using Evidence Lower-Bound evaluations from an individual model at various noising levels. We present results that are comparable to state-of-the-art Out-of-Distribution detection methods with generative models.Comment: 9 pages (main paper), 3 pages (acknowledgements & references), 3 figures, 2 tables, 1 algorithm, work accepted for BMVC 202