title: Physical metadata visualisation: The knitted personal library

authors: Hinze, Annika

subjects: design

abstract: Personal collections, be they books, music, or artefacts have significance to the owner, and provide conversation and engagement with visitors. For example visitors of physical libraries may browse the spines, select a book, and begin a conversation. When the collection is digital it cannot be engaged with in the same manner and it becomes more difficult to share and explore with others. Physical data visualisation is becoming more popular, supported by new technologies, such as 3D printing, and the resurgence of crafting. This paper reports our investigation of digital library and eBook metadata visualisation using knitting. We report on our design considerations, a digital prototype to explore knitted library shawls, and a knitted prototype for a personal digital library.

text: Physical metadata visualisation: The knitted personal library Personal collections, be they books, music, or artefacts have significance to the owner, and provide conversation and engagement with visitors. For example visitors of physical libraries may browse the spines, select a book, and begin a conversation. When the collection is digital it cannot be engaged with in the same manner and it becomes more difficult to share and explore with others. Physical data visualisation is becoming more popular, supported by new technologies, such as 3D printing, and the resurgence of crafting. This paper reports our investigation of digital library and eBook metadata visualisation using knitting. We report on our design considerations, a digital prototype to explore knitted library shawls, and a knitted prototype for a personal digital library.

year: 2022

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: Mihi (Foreword)

authors: Tuaupiki, Jackie Whetumarama

abstract: Mihi of Te Kōtihitihi - Ngā Tuhinga Reo Māori Putanga 3

text: Mihi (Foreword) Mihi of Te Kōtihitihi - Ngā Tuhinga Reo Māori Putanga 3

year: 2016