Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Expert Gate: Lifelong Learning with a Network of Experts

In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process,data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with finetuning or learning without-forgetting, can be selected. We evaluate our method on image classification and video prediction problems.Comment: CVPR 2017 pape

A postgraduate design learning experience: understanding the effects of community, cultural and contextual environment

This paper describes on going research that investigates how learning (students and tutors) takes place in a multi-disciplinary, multi-cultural postgraduate design programme in the UK. The research maps and makes explicit the effects of community, cultural and contextual environment on learning. Initial findings have identified that learning is taking place within communities of practice and further research is used to explore reasons for its emergence. The authors evaluate and discuss the effects of learning in a post disciplinary and multi-cultural environment, and its value to current design postgraduate pedagogy. A social model of learning and communities of practice is evident in the design programme studied and preliminary findings indicates that this model is particularly relevant model to adopt in the current post-disciplinary era

Developing lifelong learners: A novel online problem‐based ultrasonography subject

Online learning environments have a major role in providing lifelong learning opportunities. Lifelong learning is critical for successful participation in today's competitive work environment. This paper describes an online problem‐based learning approach to the creation of a student‐centred learning environment for the study of the biological sciences subject in the Graduate Diploma of Applied Science (Medical Ultrasonography) course at the University of Sydney. The environment is interactive and collaborative, with all communication taking place online. Students work in groups to study clinically relevant problems. A Web‐database system provides learner control in the process of knowledge acquisition, access to reference materials on the Internet and communication with the tutor and with peers through synchronous chat and asynchronous threaded discussion forums. Other online features include a protocol for problem‐solving, self‐assessment and feedback opportunities, detailed help, streaming audio and video and pre‐course, ongoing and post‐course questionnaires. This technology may be adapted to a range of disciplines and can also be utilized in on‐campus teaching