Learning with Biased Complementary Labels

In this paper, we study the classification problem in which we have access to easily obtainable surrogate for true labels, namely complementary labels, which specify classes that observations do \textbf{not} belong to. Let $Y$ and $\bar{Y}$ be the true and complementary labels, respectively. We first model the annotation of complementary labels via transition probabilities $P(\bar{Y}=i|Y=j), i\neq j\in\{1,\cdots,c\}$, where $c$ is the number of classes. Previous methods implicitly assume that $P(\bar{Y}=i|Y=j), \forall i\neq j$, are identical, which is not true in practice because humans are biased toward their own experience. For example, as shown in Figure 1, if an annotator is more familiar with monkeys than prairie dogs when providing complementary labels for meerkats, she is more likely to employ "monkey" as a complementary label. We therefore reason that the transition probabilities will be different. In this paper, we propose a framework that contributes three main innovations to learning with \textbf{biased} complementary labels: (1) It estimates transition probabilities with no bias. (2) It provides a general method to modify traditional loss functions and extends standard deep neural network classifiers to learn with biased complementary labels. (3) It theoretically ensures that the classifier learned with complementary labels converges to the optimal one learned with true labels. Comprehensive experiments on several benchmark datasets validate the superiority of our method to current state-of-the-art methods.Comment: ECCV 2018 Ora

Generalization Error in Deep Learning

Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results

Learning with a Drifting Target Concept

We study the problem of learning in the presence of a drifting target concept. Specifically, we provide bounds on the error rate at a given time, given a learner with access to a history of independent samples labeled according to a target concept that can change on each round. One of our main contributions is a refinement of the best previous results for polynomial-time algorithms for the space of linear separators under a uniform distribution. We also provide general results for an algorithm capable of adapting to a variable rate of drift of the target concept. Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates

The bodily experience of cerebral palsy: a journey to self-awareness.

PURPOSE: The aim of the study was to describe the lived bodily experience of cerebral palsy (CP). METHOD: This was a descriptive phenomenological inquiry. Ten participants were interviewed about their bodily experiences of living with CP. Interviews were semi-structured around pain and fatigue. Inductive thematic analysis was used to identify themes. RESULTS: The bodily experience of CP centered on issues of fatigue and pain as a feeling of muscle soreness. An overwhelming amount of the discussion on fatigue emphasized the fatigue that occurs with walking and prolonged activity. Self-awareness of the individuals\u27 own bodies and adapting activity to continue to participate in various aspects of their lives emerged as the most important theme. Some participants used strategies to manage their pain or fatigue; other participants were not yet fully aware of how to recognize signs of fatigue and/or how to adapt their activities. CONCLUSIONS: Self-awareness appears to be an important process to be fostered by service providers and parents. Specifically, encouraging youth with CP to be aware of their own bodies and the effects (positive and negative) of activity on pain and fatigue should be incorporated into transition programs as the individual becomes responsible for his or her own health care needs. Implications for Rehabilitation Fatigue is a major concern for some youth and young adults with cerebral palsy. Adolescents and young adults with cerebral palsy use a variety of techniques (including adapting or restricting activity and building in rest breaks) to manage fatigue. The process of self-awareness should be fostered by health care professionals leading up to and during transition from pediatric to adult care. Clinical conversations should explore the role of exercise, adaptive equipment, rest and other strategies for dealing with fatigue with a focus on understanding each client\u27s needs individually

A Heparin-Coated Circuit Reduces Complement Activation and the Release of Leukocyte Inflammatory Mediators During Extracorporeal Circulation in a Rabbit

Heparin coating modifies complement activation during extracorporeal circulation much more effcclively than systemically administered heparin. This rabbit study was undertaken to address possible mechanisms responsible for this difference. We evaluated the effect of heparin coating on complement activation and subsequently the release of leukocyte inflammatory mediators during extracorporeal circulation through a simplified circuit. We found in the heparin-coated group a significantly reduced complement hemolytic activity (CH 50 ), remaining higher leukocyte numbers, significantly decreased release of -glucuronidase, and most strikingly a complete prevention of tumor necrosis factor (TNF) formation. The significantly reduced CH 50 activity in the heparin-coated groups indicates the reduction of one or more native classical complement products. This could be explained by the absorption of complement components by the circuit, which results in reduced activity of the complement cascade. We conclude therefore that heparin coating reduces complement activation and consequently reduces the release of leukocyte inflammatory mediators.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73454/1/j.1525-1594.1992.tb00533.x.pd

Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice

The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation tasks are usually optimized for (weighted) cross-entropy. This introduces an adverse discrepancy between the learning optimization objective (the loss) and the end target metric. Recent works in computer vision have proposed soft surrogates to alleviate this discrepancy and directly optimize the desired metric, either through relaxations (soft-Dice, soft-Jaccard) or submodular optimization (Lov\'asz-softmax). The aim of this study is two-fold. First, we investigate the theoretical differences in a risk minimization framework and question the existence of a weighted cross-entropy loss with weights theoretically optimized to surrogate Dice or Jaccard. Second, we empirically investigate the behavior of the aforementioned loss functions w.r.t. evaluation with Dice score and Jaccard index on five medical segmentation tasks. Through the application of relative approximation bounds, we show that all surrogates are equivalent up to a multiplicative factor, and that no optimal weighting of cross-entropy exists to approximate Dice or Jaccard measures. We validate these findings empirically and show that, while it is important to opt for one of the target metric surrogates rather than a cross-entropy-based loss, the choice of the surrogate does not make a statistical difference on a wide range of medical segmentation tasks.Comment: MICCAI 201