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Передавальне навчання для підвищення точності класифікації візуального трансформера на обмежених даних
Get PDFThis article examines the effectiveness of pre-training generative model based on a visual transformer and subsequent fine tuning for image classification tasks. The main problem of the study is the poor training efficiency of the visual transformer on a limited amount of data. It is possible to improve the accuracy of the image classification model by using transfer learning of the knowledge obtained during the previous training of the generative model on the same data. A subset of the standard Imagenet dataset - Tiny Imagenet was used to test the hypothesis. It contains 200 categories of around 500 images each. The size of each image is 64x64 pixels. For pre-training the generative model, patches are used to mask image segments. The training of restoring masked image pixels forces the model to pay attention to the context around the removed part, as well as to general visual patterns. This leads to a better understanding of visual information by the model as a whole and helps with further fine tuning of the model for the classification task. As a result of a series of experiments, it was possible to achieve an improvement in the accuracy of image classification from 40% to 44.7%, and an analysis of the effect of the overall degree of masking and patch size on it is given. Additionally, impact of different sizes of patches (2x2, 4x4, 8x8 pixels) and different percentages of masking (20/40/60 percent) of the input image were investigated in the paper.Prombles in programming 2024; 2-3: 247-252 У цій статті досліджується ефективність попереднього навчання генеративних моделей на основі візуального трансформера і подальшому навчанню моделі для задач класифікації зображень. Основною проблемою дослідження є погана ефективність навчання візуального трансформера на обмеженій кількості даних. Можна підвищити точність моделі класифікації зображень, використавши передавальне навчання знань, отриманих під час попереднього навчання генеративної моделі на тих самих даних. Для перевірки гіпотези була використана підмножина стандартного набору даних Imagenet, що містить 200 категорій по ~500 зображень. Розмір кожного зображення 64х64 пікселів. Для попереднього навчання генеративної моделі використовуються патчі для маскування сегментів зображення. Процес навчання відновлення замаскованих пікселів зображення змушує модель звертати увагу на контекст навколо видаленої частини, а також на загальні візуальні закономірності. Це приводить до кращого розуміння моделлю візуальної інформації в цілому і допомагає у подальшому навчанні моделі під задачу класифікації. В результаті серії експериментів вдалося досягти покращення точності класифікації зображень з 40% до 44.7%, а також наведено аналіз впливу на нього загального ступеню маскування та розмірності патчів. Додатково в роботі досліджені різні розмірності патчів (2x2, 4х4, 8х8 пікселів) й різний відсоток маскування (20/40/60 відсотків) вхідного зображення та вплив цих параметрів на передавальне навчання.Prombles in programming 2024; 2-3: 247-252
Method of managing the execution of tasks of a multithreaded program according to a given dependency graph
No full textThis article examines the effectiveness of pre-training generative model based on a visual transformer and subsequent fine tuning for image classification tasks. The main problem of the study is the poor training efficiency of the visual transformer on a limited amount of data. It is possible to improve the accuracy of the image classification model by using transfer learning of the knowledge obtained during the previous training of the generative model on the same data. A subset of the standard Imagenet dataset - Tiny Imagenet was used to test the hypothesis. It contains 200 categories of around 500 images each. The size of each image is 64x64 pixels. For pre-training the generative model, patches are used to mask image segments. The training of restoring masked image pixels forces the model to pay attention to the context around the removed part, as well as to general visual patterns. This leads to a better understanding of visual information by the model as a whole and helps with further fine tuning of the model for the classification task. As a result of a series of experiments, it was possible to achieve an improvement in the accuracy of image classification from 40% to 44.7%, and an analysis of the effect of the overall degree of masking and patch size on it is given. Additionally, impact of different sizes of patches (2x2, 4x4, 8x8 pixels) and different percentages of masking (20/40/60 percent) of the input image were investigated in the paper.Prombles in programming 2024; 2-3: 247-252
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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