title: A comparison of machine learning methods for cross-domain few-shot learning

authors: Wang, Hongyu

subjects: computer science

abstract: We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and   ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to   ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model.

text: A comparison of machine learning methods for cross-domain few-shot learning We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and   ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to   ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model.

year: 2020

title: Reply to the comment by Stephenson “Discussion of de Lange, W.P. and Moon, V.G. 2005. Estimating long-term cliff recession rates from shore platform widths. Engineering Geology 80, 292–301”

authors: Moon, Vicki G.

abstract: In his discussion, Stephenson raises several points, first and foremost being that the method we outline for determining long-term cliff recession rates from shore platform widths is not universally applicable. We agree fully with this and described the assumptions required for applying the method in our paper, viz.:
1. The seaward margin of the shore platform has remained relatively static;
2. Shore platform development only occurs when the mean sea level corresponds roughly (± 1 m) to the mean elevation of the shore platform; and
3. The shore platform is not polycyclic.
If these assumptions are breached then we believe the method outlined cannot give a good result. Thus, much of our paper was devoted to justifying these assumptions for the sites studied, and we note that the use of the method at other locations would require careful consideration of the validity of the assumptions for each specific environment.
Stephenson also questions the application of static and equilibrium models and the use of the terms soft and hard rock. These will be considered in turn.

text: Reply to the comment by Stephenson “Discussion of de Lange, W.P. and Moon, V.G. 2005. Estimating long-term cliff recession rates from shore platform widths. Engineering Geology 80, 292–301” In his discussion, Stephenson raises several points, first and foremost being that the method we outline for determining long-term cliff recession rates from shore platform widths is not universally applicable. We agree fully with this and described the assumptions required for applying the method in our paper, viz.:
1. The seaward margin of the shore platform has remained relatively static;
2. Shore platform development only occurs when the mean sea level corresponds roughly (± 1 m) to the mean elevation of the shore platform; and
3. The shore platform is not polycyclic.
If these assumptions are breached then we believe the method outlined cannot give a good result. Thus, much of our paper was devoted to justifying these assumptions for the sites studied, and we note that the use of the method at other locations would require careful consideration of the validity of the assumptions for each specific environment.
Stephenson also questions the application of static and equilibrium models and the use of the terms soft and hard rock. These will be considered in turn.

year: 2008

title: Kua Tupu Te Pā Harakeke: Developing Healthy Whānau Relationships

authors: Pihama, Leonie

abstract: We have opened this chapter with our ‘pēpeha’. Pēpeha are ways through which our people introduce ourselves. These vary from tribe to tribe. The essence of pēpeha is that they link us to our tribes and all associated with that. Our mountains, our rivers, our canoes, our ancestral lines. It links us to each other. It places us, as Māori, within a wider collective consciousness and relationships. It is important to highlight that the term ‘Māori’ is a term that brings us as tribal peoples together. It is a term that our people have chosen to use as a means of unifying ourselves in the wake of the arrival of our colonizers. Prior to colonization all identification was done through our whānau (extended family structure), hapū (subtribes) or iwi (tribes). The term Māori means to be ‘normal’ or ‘pure’, as such it is a fitting term for an Indigenous People. We have, however, also been active in maintaining our hapū and iwi identities and it is through pēpeha that we can culturally share that identity with each other. Our people have been doing this for generations. And in spite of colonization seeking to undermine our cultural identity, many of us continue to do this.

text: Kua Tupu Te Pā Harakeke: Developing Healthy Whānau Relationships We have opened this chapter with our ‘pēpeha’. Pēpeha are ways through which our people introduce ourselves. These vary from tribe to tribe. The essence of pēpeha is that they link us to our tribes and all associated with that. Our mountains, our rivers, our canoes, our ancestral lines. It links us to each other. It places us, as Māori, within a wider collective consciousness and relationships. It is important to highlight that the term ‘Māori’ is a term that brings us as tribal peoples together. It is a term that our people have chosen to use as a means of unifying ourselves in the wake of the arrival of our colonizers. Prior to colonization all identification was done through our whānau (extended family structure), hapū (subtribes) or iwi (tribes). The term Māori means to be ‘normal’ or ‘pure’, as such it is a fitting term for an Indigenous People. We have, however, also been active in maintaining our hapū and iwi identities and it is through pēpeha that we can culturally share that identity with each other. Our people have been doing this for generations. And in spite of colonization seeking to undermine our cultural identity, many of us continue to do this.

year: 2012