title: Pruning feature extractor stacking for cross-domain few-shot learning

authors: Wang, Hongyu

subjects: computer science

abstract: Combining knowledge from source domains to learn efficiently from a few labelled instances in a target domain is a transfer learning problem known as cross-domain few-shot learning (CDFSL). Feature extractor stacking (FES) is a state-of-the-art CDFSL method that maintains a collection of source domain feature extractors instead of a single universal extractor. FES uses stacked generalisation to build an ensemble from extractor snapshots saved during target domain fine-tuning. It outperforms several contemporary universal model-based CDFSL methods in the Meta-Dataset benchmark. However, it incurs higher storage cost because it saves a snapshot for every fine-tuning iteration for every extractor. In this work, we propose a bidirectional snapshot selection strategy for FES, leveraging its cross-validation process and the ordered nature of its snapshots, and demonstrate that a 95% snapshot reduction can be achieved while retaining the same level of accuracy.

text: Pruning feature extractor stacking for cross-domain few-shot learning Combining knowledge from source domains to learn efficiently from a few labelled instances in a target domain is a transfer learning problem known as cross-domain few-shot learning (CDFSL). Feature extractor stacking (FES) is a state-of-the-art CDFSL method that maintains a collection of source domain feature extractors instead of a single universal extractor. FES uses stacked generalisation to build an ensemble from extractor snapshots saved during target domain fine-tuning. It outperforms several contemporary universal model-based CDFSL methods in the Meta-Dataset benchmark. However, it incurs higher storage cost because it saves a snapshot for every fine-tuning iteration for every extractor. In this work, we propose a bidirectional snapshot selection strategy for FES, leveraging its cross-validation process and the ordered nature of its snapshots, and demonstrate that a 95% snapshot reduction can be achieved while retaining the same level of accuracy.

year: 2025

title: Experiments in cross-domain few-shot learning for image classification: extended abstract

authors: Wang, Hongyu

subjects: computer science

abstract: We summarise experiments (Wang et al., 2022) evaluating cross-domain few-shot learning (CDFSL) with feature extractors trained on ImageNet. The work explores the transfer performance of extracted features on five target domains with different degrees of similarity to ImageNet. These experiments compare robust classifiers and normalisation methods, consider multi-instance learning algorithms, and evaluate the effect of using features extracted by different ResNet backbones at various levels of their convolutional hierarchies. The cosine similarity classifier and 1-vs-rest logistic regression with ℓ2 regularisation are the top-performing robust classifiers in the evaluation, and ℓ2 normalisation improves performance on all five target domains when using LDA as the robust classifier. The results also show that feature extractors with the highest capacity do not always achieve the best CDFSL performance. Lastly, simple multi-instance learning methods are shown to improve classifier accuracy.

text: Experiments in cross-domain few-shot learning for image classification: extended abstract We summarise experiments (Wang et al., 2022) evaluating cross-domain few-shot learning (CDFSL) with feature extractors trained on ImageNet. The work explores the transfer performance of extracted features on five target domains with different degrees of similarity to ImageNet. These experiments compare robust classifiers and normalisation methods, consider multi-instance learning algorithms, and evaluate the effect of using features extracted by different ResNet backbones at various levels of their convolutional hierarchies. The cosine similarity classifier and 1-vs-rest logistic regression with ℓ2 regularisation are the top-performing robust classifiers in the evaluation, and ℓ2 normalisation improves performance on all five target domains when using LDA as the robust classifier. The results also show that feature extractors with the highest capacity do not always achieve the best CDFSL performance. Lastly, simple multi-instance learning methods are shown to improve classifier accuracy.

year: 2022

title: The intergenerational perpetuation of achievement messages in whānau

authors: Southey, Kim Marie

subjects: Maori

abstract: The current research is an open exploration of achievement messages that are carried from generation to generation in whānau, in relation to the mainstream education system of Aotearoa. Participant groups comprised of two to three generations within each whānau. A maximum of eight whānau will be asked to participate. A series of continuous conversations will be held with each member of the whānau, reminiscent of the narrative approach, but most recently described as a methodology that allows for ensuring that data is collected in its fullest form.
Differing views about achievement and how it is negotiated within whānau will be explored; along with issues on what, and who, within whānau are the most predominant indicators of achievement views. Detracting from an ethnocentric view of scholastic ability (a common marker for achievement) is done through abandoning mainstream ideals. This research favours an open exploration approach allowing for differing values about what constitutes achievement, and what context achievement is based in. Outcomes of the research are intended to show patterns within whānau and among Māori on the mainstream school system, and how this system has contributed to achievement messages. The positive or negative nature of the contribution from mainstream education is highly important and relevant to further research goals, including the ability to use outcomes to suggest social change in education provision for Māori.

text: The intergenerational perpetuation of achievement messages in whānau The current research is an open exploration of achievement messages that are carried from generation to generation in whānau, in relation to the mainstream education system of Aotearoa. Participant groups comprised of two to three generations within each whānau. A maximum of eight whānau will be asked to participate. A series of continuous conversations will be held with each member of the whānau, reminiscent of the narrative approach, but most recently described as a methodology that allows for ensuring that data is collected in its fullest form.
Differing views about achievement and how it is negotiated within whānau will be explored; along with issues on what, and who, within whānau are the most predominant indicators of achievement views. Detracting from an ethnocentric view of scholastic ability (a common marker for achievement) is done through abandoning mainstream ideals. This research favours an open exploration approach allowing for differing values about what constitutes achievement, and what context achievement is based in. Outcomes of the research are intended to show patterns within whānau and among Māori on the mainstream school system, and how this system has contributed to achievement messages. The positive or negative nature of the contribution from mainstream education is highly important and relevant to further research goals, including the ability to use outcomes to suggest social change in education provision for Māori.

year: 2003