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

title: He pūrongo whakahokinga kōrero a te akomanga rūmaki o Te Tohu Paetahi

authors: Nock, Sophie

subjects: Reo Māori

abstract: Ko te whāinga matua o tēnei rangahau he whakakaokao, he wetewete āno hoki i ngā whakahokinga kōrero a te hunga uiui i kake atu ai ki runga i te waka o Te Tohu Paetahi i te tau 2008, i raro i te kōawa o Rehutai, i raro anō hoki i te maru o Te Whare Wānanga o Waikato. Ka aro atu ki ngā urupare a te hunga tekau mā ono mō te kaupapa nei o Te Tohu Paetahi, ka mutu, ka matapakihia ngā kōrero me ngā whakaaro o taua hunga kia āta kitea ai, āe rānei e tika ana te whakahaere, te mahi whakaako i te reo, te tautoko mai hoki a ngā kaiwhakaako me te kura. I te mutunga iho tautoko katoa te hunga uiui ki aua mea katoa.

text: He pūrongo whakahokinga kōrero a te akomanga rūmaki o Te Tohu Paetahi Ko te whāinga matua o tēnei rangahau he whakakaokao, he wetewete āno hoki i ngā whakahokinga kōrero a te hunga uiui i kake atu ai ki runga i te waka o Te Tohu Paetahi i te tau 2008, i raro i te kōawa o Rehutai, i raro anō hoki i te maru o Te Whare Wānanga o Waikato. Ka aro atu ki ngā urupare a te hunga tekau mā ono mō te kaupapa nei o Te Tohu Paetahi, ka mutu, ka matapakihia ngā kōrero me ngā whakaaro o taua hunga kia āta kitea ai, āe rānei e tika ana te whakahaere, te mahi whakaako i te reo, te tautoko mai hoki a ngā kaiwhakaako me te kura. I te mutunga iho tautoko katoa te hunga uiui ki aua mea katoa.

year: 2012-05

title: Feature extractor stacking for cross-domain few-shot learning

authors: Wang, Hongyu

subjects: computer science

abstract: Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor. This enables efficient inference but necessitates re-computation of the extractor whenever a new source domain is added. Some of these methods are also incompatible with heterogeneous source domain extractor architectures. We propose feature extractor stacking (FES), a new CDFSL method for combining information from a collection of extractors, that can utilise heterogeneous pretrained extractors out of the box and does not maintain a universal model that needs to be re-computed when its extractor collection is updated. We present the basic FES algorithm, which is inspired by the classic stacked generalisation approach, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each extractor independently, use cross-validation to extract training data for stacked generalisation from the support set, and learn a simple linear stacking classifier from this data. We evaluate our FES methods on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that they can achieve state-of-the-art performance.

text: Feature extractor stacking for cross-domain few-shot learning Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor. This enables efficient inference but necessitates re-computation of the extractor whenever a new source domain is added. Some of these methods are also incompatible with heterogeneous source domain extractor architectures. We propose feature extractor stacking (FES), a new CDFSL method for combining information from a collection of extractors, that can utilise heterogeneous pretrained extractors out of the box and does not maintain a universal model that needs to be re-computed when its extractor collection is updated. We present the basic FES algorithm, which is inspired by the classic stacked generalisation approach, and also introduce two variants: convolutional FES (ConFES) and regularised FES (ReFES). Given a target-domain task, these algorithms fine-tune each extractor independently, use cross-validation to extract training data for stacked generalisation from the support set, and learn a simple linear stacking classifier from this data. We evaluate our FES methods on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that they can achieve state-of-the-art performance.

year: 2023-11-30