Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias has a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) Using the presented methodology we reveal that the VGG-16 architecture outperforms the AlexNet architecture at face recognition tasks because it can much better generalize to unseen face poses, although it has significantly more parameters. 4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation. 5) We demonstrate that our findings on synthetic data also apply when learning from real-world data. Our face image generator is publicly available to enable the community to benchmark other DCNN architectures.Comment: Accepted to CVPR 2018 Workshop on Analysis and Modeling of Faces and Gestures (AMFG

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects. Generative approaches to computer vision allow us to overcome this difficulty by explicitly modeling the physical image formation process. Using generative object models, the analysis of an observed image is performed via Bayesian inference of the posterior distribution. This conceptually simple approach tends to fail in practice because of several difficulties stemming from sampling the posterior distribution: high-dimensionality and multi-modality of the posterior distribution as well as expensive simulation of the rendering process. The main difficulty of sampling approaches in a computer vision context is choosing the proposal distribution accurately so that maxima of the posterior are explored early and the algorithm quickly converges to a valid image interpretation. In this work, we propose to use a Bayesian Neural Network for estimating an image dependent proposal distribution. Compared to a standard Gaussian random walk proposal, this accelerates the sampler in finding regions of the posterior with high value. In this way, we can significantly reduce the number of samples needed to perform facial image analysis.Comment: Accepted to the Bayesian Deep Learning Workshop at NeurIPS 201

Temporal changes in programme outcomes among adult patients initiating antiretroviral therapy across South Africa, 2002-2007.

OBJECTIVE: Little is known about the temporal impact of the rapid scale-up of large antiretroviral therapy (ART) services on programme outcomes. We describe patient outcomes [mortality, loss-to-follow-up (LTFU) and retention] over time in a network of South African ART cohorts. DESIGN: Cohort analysis utilizing routinely collected patient data. METHODS: Analysis included adults initiating ART in eight public sector programmes across South Africa, 2002-2007. Follow-up was censored at the end of 2008. Kaplan-Meier methods were used to estimate time to outcomes, and proportional hazards models to examine independent predictors of outcomes. RESULTS: Enrolment (n = 44 177, mean age 35 years; 68% women) increased 12-fold over 5 years, with 63% of patients enrolled in the past 2 years. Twelve-month mortality decreased from 9% to 6% over 5 years. Twelve-month LTFU increased annually from 1% (2002/2003) to 13% (2006). Cumulative LTFU increased with follow-up from 14% at 12 months to 29% at 36 months. With each additional year on ART, failure to retain participants was increasingly attributable to LTFU compared with recorded mortality. At 12 and 36 months, respectively, 80 and 64% of patients were retained. CONCLUSION: Numbers on ART have increased rapidly in South Africa, but the programme has experienced deteriorating patient retention over time, particularly due to apparent LTFU. This may represent true loss to care, but may also reflect administrative error and lack of capacity to monitor movements in and out of care. New strategies are needed for South Africa and other low-income and middle-income countries to improve monitoring of outcomes and maximize retention in care with increasing programme size

Forgiveness takes place on an attitudinal continuum from hostility to friendliness: Toward a closer union of forgiveness theory and measurement.

Researchers commonly conceptualize forgiveness as a rich complex of psychological changes involving attitudes, emotions, and behaviors. Psychometric work with the measures developed to capture this conceptual richness, however, often points to a simpler picture of the psychological dimensions in which forgiveness takes place. In an effort to better unite forgiveness theory and measurement, we evaluate several psychometric models for common measures of forgiveness. In doing so, we study people from the United States and Japan to understand forgiveness in both nonclose and close relationships. In addition, we assess the predictive utility of these models for several behavioral outcomes that traditionally have been linked to forgiveness motives. Finally, we use the methods of item response theory, which place person abilities and item responses on the same metric and, thus, help us draw psychological inferences from the ordering of item difficulties. Our results highlight models based on correlated factors models and bifactor (S-1) models. The bifactor (S-1) model evinced particular utility: Its general factor consistently predicts variation in relevant criterion measures, including 4 different experimental economic games (when played with a transgressor), and also suffuses a second self-report measure of forgiveness. Moreover, the general factor of the bifactor (S-1) model identifies a single psychological dimension that runs from hostility to friendliness while also pointing to other sources of variance that may be conceived of as method factors. Taken together, these results suggest that forgiveness can be usefully conceptualized as prosocial change along a single attitudinal continuum that ranges from hostility to friendliness. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data

It is well known that deep learning approaches to facerecognition suffer from various biases in the available train-ing data. In this work, we demonstrate the large potentialof synthetic data for analyzing and reducing the negativeeffects of dataset bias on deep face recognition systems. Inparticular we explore two complementary application areasfor synthetic face images: 1) Using fully annotated syntheticface images we can study the face recognition rate as afunction of interpretable parameters such as face pose. Thisenables us to systematically analyze the effect of differenttypes of dataset biases on the generalization ability of neu-ral network architectures. Our analysis reveals that deeperneural network architectures can generalize better to un-seen face poses. Furthermore, our study shows that currentneural network architectures cannot disentangle face poseand facial identity, which limits their generalization ability.2) We pre-train neural networks with large-scale syntheticdata that is highly variable in face pose and the number offacial identities. After a subsequent fine-tuning with real-world data, we observe that the damage of dataset bias inthe real-world data is largely reduced. Furthermore, wedemonstrate that the size of real-world datasets can be re-duced by 75% while maintaining competitive face recogni-tion performance. The data and software used in this workare publicly available

Perceptions on competence by design in urology

Introduction: The Royal College of Physicians and Surgeons of Canada has begun implementing Competence by Design (CBD). However, it is unclear how much urology trainees and faculty know about CBD, their attitudes towards this change, and their willingness to embrace and participate in this new model of training. Methods: This cross-sectional study was conducted through an online survey, which was administered to all trainees and faculty at Canadian urology programs prior to the implementation of CBD. The final survey consisted of eight demographic questions, 17 five-point Likert items, one visual analog scale question, 11 multiple-choice questions, and two open-ended questions. Results: A total of 74 participants (38 faculty and 36 trainees) across 12 universities responded, with a completion rate of 82.4%. This corresponded to an overall response rate of 20.5%. Overall, there was a lack of resounding enthusiasm towards this shift to CBD in urology. Although both trainees and faculty had overall positive perceptions of CBD on assessment, teaching, and readiness, most agreed that this transition will be costly and associated with increased requirements for time, funding, and administrative support. Furthermore, there were significant concerns regarding the lack of valid assessment tools and evidence for the validity of entrustable professional activities. Conclusions: While this survey has demonstrated an appreciation for the benefits of CBD, challenges are equally anticipated. CBD in urology will be a fertile research area; this study has identified several important educational questions regarding the model\u27s effectiveness and consequences, thus, providing collaborative opportunities among all Canadian programs

Greedy Structure Learning of Hierarchical Compositional Models

In this work, we consider the problem of learning a hierarchical generative model of an object from a set of im-ages which show examples of the object in the presenceof variable background clutter. Existing approaches tothis problem are limited by making strong a-priori assump-tions about the object’s geometric structure and require seg-mented training data for learning. In this paper, we pro-pose a novel framework for learning hierarchical compo-sitional models (HCMs) which do not suffer from the men-tioned limitations. We present a generalized formulation ofHCMs and describe a greedy structure learning frameworkthat consists of two phases: Bottom-up part learning andtop-down model composition. Our framework integratesthe foreground-background segmentation problem into thestructure learning task via a background model. As a result, we can jointly optimize for the number of layers in thehierarchy, the number of parts per layer and a foreground-background segmentation based on class labels only. Weshow that the learned HCMs are semantically meaningfuland achieve competitive results when compared to othergenerative object models at object classification on a stan-dard transfer learning dataset