Data augmentation的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包

News Nerds: Institutional Change in Journalism

為了解決Data augmentation的問題，作者 這樣論述：

The content of news has not changed much over the last century--politicians, celebrities, wars, crime, and sports dominate past and present headlines. Yet, the ways in which journalists both gather and disseminate information have been turned on their head. Gone are the days of editors assigning

stories to writers, who then research, inquire, and present what they found in a compelling yet accurate fashion. Today’s journalists are coding, programming, running analytics, and developing apps. These news nerds are industry professionals working in jobs at the intersection of traditional journa

lism and technologically intensive positions that were once largely separate. Consequently, news nerds have changed the institutionalized view of journalism, which now accounts for these professionals. News Nerds explores how technological, economic, and societal changes are impacting the institutio

nalized profession of journalism. Allie Kosterich draws on a mixed-methods research design that blends interviews, social network analysis of LinkedIn data, job postings, and industry publications to make sense of how skills and practices become entrenched throughout the news industry. Taken togethe

r, these data reveal the ways in which the profession is evolving to incorporate new technological skillsets and new routines of production. In telling these stories and sharing these findings, Kosterich directly confronts what happens when new skillsets and new ways of understanding and producing n

ews start to collide with the old routines of journalism. News Nerds introduces the notion of institutional augmentation--a process of institutional change that is not restricted to the expected binary outcome of the reinstitutionalization of something new or failure as a fleeting fad. Instead, as i

n the case of news nerds and journalism, there exists an alternative possibility in the coexistence of supplementary institutions. News Nerds provides a timely and relevant analysis of contemporary journalism and a model for understanding how industries react to the emergence of new career trajector

ies and new categories of employment.

News Nerds: Institutional Change in Journalism

為了解決Data augmentation的問題，作者 這樣論述：

The content of news has not changed much over the last century--politicians, celebrities, wars, crime, and sports dominate past and present headlines. Yet, the ways in which journalists both gather and disseminate information have been turned on their head. Gone are the days of editors assigning

stories to writers, who then research, inquire, and present what they found in a compelling yet accurate fashion. Today’s journalists are coding, programming, running analytics, and developing apps. These news nerds are industry professionals working in jobs at the intersection of traditional journa

lism and technologically intensive positions that were once largely separate. Consequently, news nerds have changed the institutionalized view of journalism, which now accounts for these professionals. News Nerds explores how technological, economic, and societal changes are impacting the institutio

nalized profession of journalism. Allie Kosterich draws on a mixed-methods research design that blends interviews, social network analysis of LinkedIn data, job postings, and industry publications to make sense of how skills and practices become entrenched throughout the news industry. Taken togethe

r, these data reveal the ways in which the profession is evolving to incorporate new technological skillsets and new routines of production. In telling these stories and sharing these findings, Kosterich directly confronts what happens when new skillsets and new ways of understanding and producing n

ews start to collide with the old routines of journalism. News Nerds introduces the notion of institutional augmentation--a process of institutional change that is not restricted to the expected binary outcome of the reinstitutionalization of something new or failure as a fleeting fad. Instead, as i

n the case of news nerds and journalism, there exists an alternative possibility in the coexistence of supplementary institutions. News Nerds provides a timely and relevant analysis of contemporary journalism and a model for understanding how industries react to the emergence of new career trajector

ies and new categories of employment.

基於維持局部結構與特徵⼀致性之改善點雲語意分割方法

為了解決Data augmentation的問題，作者林正偉 這樣論述：

現今有許多研究探討如何運用深度學習方法處理三維點雲 (Point Cloud)， 雖然有些研究成功轉換二維卷積網路到三維空間，或利用多層感知機 (MLP) 處理點雲，但在點雲語意分割 (semantic segmentation) 上仍無法到 達如同二維語意分割的效能。其中一個重要因素是三維資料多了空間維度， 且缺乏如二維研究擁有龐大的資料集，以致深度學習模型難以最佳化和容 易過擬合 (overfit)。為了解決這個問題，約束網路學習的方向是必要的。在 此篇論文中，我們專注於研究點雲語意分割，基於輸入點會和擁有相似局部 構造的相鄰點擁有相同的語意類別，提出一個藉由比較局部構造，約束相鄰 區域

特徵差異的損失函數，使模型學習局部結構和特徵之間的一致性。為了 定義局部構造的相似性，我們提出了兩種提取並比較局部構造的方法，以此 實作約束局部結構和特徵間一致性的損失函數。我們的方法在兩個不同的 室內、外資料集顯著提升基準架構 (baseline) 的效能，並在 S3DIS 中取得 目前最好的結果。我們也提供透過此篇論文方法訓練後的網路，在輸入點與 相鄰點特徵間差異的視覺化結果。