title: Transfer of pretrained model weights substantially improves semi-supervised image classification

authors: Sahito, Attaullah

subjects: computer science

abstract: Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labeled examples requires considerable resources, time, and effort. If labeling new data is not feasible, so-called semi-supervised learning can achieve better generalisation than purely supervised learning by employing unlabeled instances as well as labeled ones. The work presented in this paper is motivated by the observation that transfer learning provides the opportunity to potentially further improve performance by exploiting models pretrained on a similar domain. More specifically, we explore the use of transfer learning when performing semi-supervised learning using self-learning. The main contribution is an empirical evaluation of transfer learning using different combinations of similarity metric learning methods and label propagation algorithms in semi-supervised learning. We find that transfer learning always substantially improves the model’s accuracy when few labeled examples are available, regardless of the type of loss used for training the neural network. This finding is obtained by performing extensive experiments on the SVHN, CIFAR10, and Plant Village image classification datasets and applying pretrained weights from Imagenet for transfer learning.

text: Transfer of pretrained model weights substantially improves semi-supervised image classification Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labeled examples requires considerable resources, time, and effort. If labeling new data is not feasible, so-called semi-supervised learning can achieve better generalisation than purely supervised learning by employing unlabeled instances as well as labeled ones. The work presented in this paper is motivated by the observation that transfer learning provides the opportunity to potentially further improve performance by exploiting models pretrained on a similar domain. More specifically, we explore the use of transfer learning when performing semi-supervised learning using self-learning. The main contribution is an empirical evaluation of transfer learning using different combinations of similarity metric learning methods and label propagation algorithms in semi-supervised learning. We find that transfer learning always substantially improves the model’s accuracy when few labeled examples are available, regardless of the type of loss used for training the neural network. This finding is obtained by performing extensive experiments on the SVHN, CIFAR10, and Plant Village image classification datasets and applying pretrained weights from Imagenet for transfer learning.

year: 2020

title: ‘Against the grain’: over 75 years and joining a community exercise programme

authors: Grant, Bevan C.

subjects: ageing

abstract: This study explores the meanings that 22 men over 75 years attributed to their experience when joining a community exercise programme. Information gathered via interviews was analysed by inductive analysis. Four dominant themes emerged and these provide a framework for representing the findings. All men believed a physical active lifestyle was desirable for well-being and quality of life, although engagement at various levels is strongly influenced by the social construction of ageing and one’s personal history. Being physically active via a programme in later life is as much an embodied as a technical endeavour. It is also as much biographical as biological, and for ongoing engagement exercise must be deemed purposeful to self, irrespective of whether or not what occurs is in accordance with the scientific script prescribed for optimal health. Just being ‘older’ poses a variety of challenges to adopting a physically active lifestyle in the later years, something the men in this study imply needs to be better understood.

text: ‘Against the grain’: over 75 years and joining a community exercise programme This study explores the meanings that 22 men over 75 years attributed to their experience when joining a community exercise programme. Information gathered via interviews was analysed by inductive analysis. Four dominant themes emerged and these provide a framework for representing the findings. All men believed a physical active lifestyle was desirable for well-being and quality of life, although engagement at various levels is strongly influenced by the social construction of ageing and one’s personal history. Being physically active via a programme in later life is as much an embodied as a technical endeavour. It is also as much biographical as biological, and for ongoing engagement exercise must be deemed purposeful to self, irrespective of whether or not what occurs is in accordance with the scientific script prescribed for optimal health. Just being ‘older’ poses a variety of challenges to adopting a physically active lifestyle in the later years, something the men in this study imply needs to be better understood.

year: 2012

title: Maori & Psychology Research Unit annual report 2005

authors: Rua, Mohi

subjects: Maori and Psychology Research Unit

abstract: Annual report of the Maori and Psychology Research Unit (MPRU) 2005. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

text: Maori & Psychology Research Unit annual report 2005 Annual report of the Maori and Psychology Research Unit (MPRU) 2005. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

year: 2005