Deep Unsupervised Similarity Learning using Partially Ordered Sets

Unsupervised learning of visual similarities is of paramount importance to computer vision, particularly due to lacking training data for fine-grained similarities. Deep learning of similarities is often based on relationships between pairs or triplets of samples. Many of these relations are unreliable and mutually contradicting, implying inconsistencies when trained without supervision information that relates different tuples or triplets to each other. To overcome this problem, we use local estimates of reliable (dis-)similarities to initially group samples into compact surrogate classes and use local partial orders of samples to classes to link classes to each other. Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes. Adopting a strategy of self-supervision, a CNN is trained to optimally represent samples in a mutually consistent manner while updating the classes. The similarity learning and grouping procedure are integrated in a single model and optimized jointly. The proposed unsupervised approach shows competitive performance on detailed pose estimation and object classification.Comment: Accepted for publication at IEEE Computer Vision and Pattern Recognition 201

The Indirect Effects of Participative and Abusive Supervisions on Talent Development through Clinical Learning Environment

This chapter aims to examine the indirect effect of clinical learning environment in the relationship between supervisory styles (participative and abusive supervisions) and talent development in the healthcare setting. A questionnaire-based survey was implemented to collect the data. The data was collected from 355 junior doctors in six Malaysian public hospitals. The partial least squares based structural equation modeling (PLS-SEM) was used to test the hypotheses. The main findings are: (1) clinical learning environment has a strong positive indirect effect on the participative supervision-talent development link. This reveals that a conducive clinical learning environment that allows empowerment leads to talent development and (2) clinical learning environment has a strong negative indirect effect on the abusive supervision-talent development link. This implies that junior doctors who feel abused have reduced capacity to work and participate in the learning environment which consequently affects their talent development. The result of this study is consistent with theoretical propositions that clinical learning environment indirectly affects the relationship between participative supervision-talent development and abusive supervision-talent development. This study contributes to the clinical learning environment literature by providing empirical support towards identifying clinical learning environment as the underlying mechanism that accounts for the participative supervision-talent development and abusive supervision-talent development relationships

Contribution of academic supervision to vocational students’ learning readiness

The learning process in vocational schools has different characteristics compared to that of the non-vocational. Students’ readiness is one significant variable in determining students’ learning success. Hence, identifying the antecedent of the variable is necessary. The research aimed to measure the contribution of academic supervision through teachers' professional and pedagogic competence and its impact on vocational school students’ learning readiness. The quantitative research employed ex-post facto design with partial least square equation modelling (PLS-SEM) to test the hypothesis. Non-probability sampling, particularly purposive sampling, was used to take the samples, which were 71 teachers and 96 students in three private vocational schools in Gunung Kidul Regency, Indonesia. Meanwhile, the data were analyzed using PLS-SEM because the study involved less than 100 samples. The results showed pedagogic competence contributes to learning readiness, professional competence does not contribute to learning readiness, academic supervision contributes to pedagogic competence, and academic supervision contributes to professional competence. Besides, indirect effect scores concluded two points: academic supervision through teachers’ professional competence contributes to learning readiness and academic supervision through a teacher’s professional competence does not contribute to learning readiness. The principals and teachers can use the findings to improve their performance at school and in the classroom

Learning with Partial Supervision for Clustering and Classification

In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not have the full label but only partial supervision about the data, such as instance similarities or incomplete label assignments. In such cases, traditional clustering and classification methods do not directly apply. To address such problems, this thesis focuses on the task of learning from partial supervision for clustering and classification tasks. For clustering with partial supervision, we investigate three problems: a) constrained clustering in multi-instance multi-label learning, where the goal is to group instances into clusters that respect the background knowledge given by the bag-level labels; b) clustering with constraints, where the partial supervision is expressed as "pairwise constraints" or "relative constraints", regarding similarities about instance pairs and triplets respectively; c) active learning of pairwise constraints for clustering, where the goal is to improve the clustering with minimum human effort by iteratively querying the most informative pairs to an oracle. For classification with partial supervision, we address the problem of multi-label learning where data is associated with a latent label hierarchy and incomplete label assignments, and the goal is to simultaneously discover the latent hierarchy as well as to learn a multi-label classifier that is consistent with the hierarchy.Keywords: Classification, Partial Supervision, Active Learning, Clusterin

Active Labeling: Streaming Stochastic Gradients

The workhorse of machine learning is stochastic gradient descent. To access stochastic gradients, it is common to consider iteratively input/output pairs of a training dataset. Interestingly, it appears that one does not need full supervision to access stochastic gradients, which is the main motivation of this paper. After formalizing the "active labeling" problem, which focuses on active learning with partial supervision, we provide a streaming technique that provably minimizes the ratio of generalization error over the number of samples. We illustrate our technique in depth for robust regression.Comment: 38 pages (9 main pages), 9 figure