SurReal: enhancing Surgical simulation Realism using style transfer

Surgical simulation is an increasingly important element of surgical education. Using simulation can be a means to address some of the significant challenges in developing surgical skills with limited time and resources. The photo-realistic fidelity of simulations is a key feature that can improve the experience and transfer ratio of trainees. In this paper, we demonstrate how we can enhance the visual fidelity of existing surgical simulation by performing style transfer of multi-class labels from real surgical video onto synthetic content. We demonstrate our approach on simulations of cataract surgery using real data labels from an existing public dataset. Our results highlight the feasibility of the approach and also the powerful possibility to extend this technique to incorporate additional temporal constraints and to different applications

Missing Value Imputation With Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a technique for unsupervised learning called Unsupervised Backpropagation (UBP), which trains a multi-layer perceptron to fit to the manifold sampled by a set of observed point-vectors. We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 datasets and 9 supervised learning algorithms that classification accuracy is usually higher when randomly-withheld values are imputed using UBP, rather than with other methods

Multi-component Image Translation for Deep Domain Generalization

Domain adaption (DA) and domain generalization (DG) are two closely related methods which are both concerned with the task of assigning labels to an unlabeled data set. The only dissimilarity between these approaches is that DA can access the target data during the training phase, while the target data is totally unseen during the training phase in DG. The task of DG is challenging as we have no earlier knowledge of the target samples. If DA methods are applied directly to DG by a simple exclusion of the target data from training, poor performance will result for a given task. In this paper, we tackle the domain generalization challenge in two ways. In our first approach, we propose a novel deep domain generalization architecture utilizing synthetic data generated by a Generative Adversarial Network (GAN). The discrepancy between the generated images and synthetic images is minimized using existing domain discrepancy metrics such as maximum mean discrepancy or correlation alignment. In our second approach, we introduce a protocol for applying DA methods to a DG scenario by excluding the target data from the training phase, splitting the source data to training and validation parts, and treating the validation data as target data for DA. We conduct extensive experiments on four cross-domain benchmark datasets. Experimental results signify our proposed model outperforms the current state-of-the-art methods for DG.Comment: Accepted in WACV 201

The development of the five mini-theories of self-determination theory: an historical overview, emerging trends, and future directions

Self-determination theory is a macro-theory of human motivation, emotion, and personality that has been under development for 40 years following the seminal work of Edward Deci and Richard Ryan. Self-determination theory (SDT; Deci & Ryan, 1985b, 2000; Niemiec, Ryan, & Deci, in press; Ryan & Deci, 2000; Vansteenkiste, Ryan, & Deci, 2008) has been advanced in a cumulative, research-driven manner, as new ideas have been naturally and steadily integrated into the theory following sufficient empirical support, which has helped SDT maintain its internal consistency. To use a metaphor, the development of SDT is similar to the construction of a puzzle. Over the years, new pieces have been added to the theory once their fit was determined. At present, dozens of scholars throughout the world continue to add their piece to the ‘‘SDT puzzle,’’ and hundreds of practitioners working with all age groups, and in various domains and cultures, have used SDT to inform their practice. Herein, we provide an historical overview of the development of the five mini-theories (viz., cognitive evaluation theory, organismic integration theory, causality orientations theory, basic needs theory, and goal content theory) that constitute SDT, discuss emerging trends within those mini-theories, elucidate similarities with and differences from other theoretical frameworks, and suggest directions for future researc