Relevant bibliographies by topics / Generation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Generation'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Generation"

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Keeling, Sarah. "Advising the Millennial Generation." NACADA Journal 23, no. 1-2 (March 1, 2003): 30–36. http://dx.doi.org/10.12930/0271-9517-23.1-2.30.

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In Generations: The History of America's Future, 1584 to 2069 (1991), William Strauss and Neil Howe articulated their idea of generations. In Millennials Rising: The Next Great Generation 2000, they expanded significantly on the Millennial generation. The first wave of Millennials hit college campuses in the fall of 2000. These students will leave their mark on higher education and academic advising. Academic advisors aware of generational patterns and tendencies will be more effective when working with students. In this article, I discuss the concept of generations, define the Millennial generation, discuss this generation's impact on academic advising, and propose some recommendations.
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Möhr, J. R. "The Generation of Generations." Methods of Information in Medicine 24, no. 02 (April 1985): 55–56. http://dx.doi.org/10.1055/s-0038-1635363.

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Bhatia, Rahul, Vishakha Gautam, and Yash Kumar Ankush Garg. "Dynamic Question Answer Generator: An Enhanced Approach to Question Generation." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 785–89. http://dx.doi.org/10.31142/ijtsrd23730.

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Shin, Hayoung, and Sungbum Cho. "MZ Generation's Perception of University Evaluation: Focusing on the Difference Between Generations." Korean Society of Culture and Convergence 45, no. 1 (January 31, 2023): 497–510. http://dx.doi.org/10.33645/cnc.2023.01.45.01.497.

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The purpose of this study is to analyze the awareness about unverisyt of MZ generation (millennials and Z generation) comparing the other generatios, For this study, questions related to awareness, perception, and demand for university evaluation were independently developed, and 1,458 people were randomly sampled across all generations from their 10s to 90s, and a survey was conducted. The MZ generation group of 859 (58.9%) revealed a statistically significant difference in awareness level from other generations regarding the publicity, reliability, and accountability of the university. Through the results of this study, the direction of improvement of the university evaluation system and the plan to raise awareness were suggested.; based on the perception about the university evaluation system and university evaluation elements of MZ generation; as direct stakeholders and beneficiaries as students and graduates.
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DURMAZ, Şerife, and Bülent OKUMUŞ. "GENERATION DIFFERENCES IN BUSINESS LIFE AND GENERATION Z." IEDSR Association 6, no. 16 (November 15, 2021): 47–68. http://dx.doi.org/10.46872/pj.448.

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For successful interaction and communication to occur in organizations, it is necessary to understand the characteristics of each generation and what motivates them to be successful and good employees. In an organization, many of the employees' values and attitudes towards work can be quite different from previous generations, and managers need to take these generational differences into account in order to effectively manage the workforce. Strauss and Howe (1991) identified eighteen generations throughout four centuries of American history, dating back to the first New World colonizers. In this study, the differences in working orientations and attitudes between generations within the scope of the Traditional Generation, Baby Boomer Generation, X Generation, Y Generation and Z Generation, which are the generations that affect / will affect today's working life, are discussed. After explaining what kind of environment these generations were born into and how they were affected by social, economic and environmental conditions, it was examined how these generations achieved unity in their working environments. It has been contributed to the recognition of Generation Z by their managers and colleagues, who have just entered or will enter the labor market, and to learn what kind of working group they are/will be faced with, and suggestions are made for the future labor markets.
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Wang, Xi Bo, Ya Lin Lei, and Min Yao. "China's Thermal Power Generation Forecasting Based on Generalized Weng Model." Advanced Materials Research 960-961 (June 2014): 503–9. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.503.

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Since the 21st century, China's power industry has been developing very quickly, and the generated electrical energy has been growing rapidly. Although nuclear power, wind power, solar power generations have been increased, thermal power generation still accounts for more than 80% of the total generating capacity. Thermal power provides an important material basis for the development of the national economy. Therefore, the prediction research on China's thermal power generation trend is becoming a topic of great interest. The fuel of thermal power generation-coal, is an exhaustible resource. Due to the exhaustible constraints the fuel, thermal power generation trend is bound to show a similar trend bell curve as the coal production trend, similar to a bell-shaped curve—a gradual increase to maximum output and then a short peak and a gradual decline. To get more accurate results of future thermal power generation, this paper applies the generalized Weng model to forecast China's thermal power generation peak and trend. The result indicted that the peak of China's thermal power generation appears in 2022 with generating capacity of 51,702 TWh. The generating capacity of thermal power will decrease gradually after 2022. Based on the results, the paper proposes some policy recommendations for the sustainable development of China's electrical energy. China should decrease the percentage of the capacity which comes from thermal generation and reduce the dependence on thermal power generation. Moreover, nuclear, hydraulic, wind and solar power should be developed before the thermal power generation peak.
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Hunter, J. Paul. "Canon of Generations, Generation of Canons." Modern Language Studies 18, no. 1 (1988): 38. http://dx.doi.org/10.2307/3194699.

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Candranegara, I. Made Wimas, I. Putu Eka Mahardhika, and I. Wayan Mirta. "Partisipasi Generasi Milenial dalam Kancah Politik Nasional 2019." Jurnal Bali Membangun Bali 2, no. 1 (April 26, 2018): 21–30. http://dx.doi.org/10.51172/jbmb.v2i1.36.

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Millennials generations are valuable assets owned by the Indonesian people. The number of generations reaching approximately 35% now makes every politician interested in getting feedback from this generation. The millennial generation currently has a very broad momentum, one of which is in terms of political participation in Indonesia. The activity of this millennial generation is able to change the direction and pace of Indonesian politics towards being identical to this generation's habit. His appearance to be active in political participation today has changed Indonesia's political paradigm a lot. Young figures with very different communication styles compared to their predecessor's style make this millennial generation has its own distinctive characteristics and colors in the Indonesian public sphere. Besides that Indonesia, which is currently entering the gate of industrial revolution 4.0, must certainly adapt to the pace of the era and change the paradigm of a very fast generation especially in 2030 Indonesia will experience a demographic bonus, so that readiness and maturity in the political world for the millennial generation is very important to prepare since early.
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Kumar, Naveen, Vaibhav Setia, and Prakhar Bajpai. "Analysis of Energy Generation from Exhaust of Automobile using Peltier Thermoelectric Generator." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 749–51. http://dx.doi.org/10.31142/ijtsrd22986.

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Smithson, Mark, Jennifer L. M. Thorson, Ingrid Sadler-Riggleman, Daniel Beck, Michael K. Skinner, and Mark Dybdahl. "Between-Generation Phenotypic and Epigenetic Stability in a Clonal Snail." Genome Biology and Evolution 12, no. 9 (September 1, 2020): 1604–15. http://dx.doi.org/10.1093/gbe/evaa181.

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Abstract Epigenetic variation might play an important role in generating adaptive phenotypes by underpinning within-generation developmental plasticity, persistent parental effects of the environment (e.g., transgenerational plasticity), or heritable epigenetically based polymorphism. These adaptive mechanisms should be most critical in organisms where genetic sources of variation are limited. Using a clonally reproducing freshwater snail (Potamopyrgus antipodarum), we examined the stability of an adaptive phenotype (shell shape) and of DNA methylation between generations. First, we raised three generations of snails adapted to river currents in the lab without current. We showed that habitat-specific adaptive shell shape was relatively stable across three generations but shifted slightly over generations two and three toward a no-current lake phenotype. We also showed that DNA methylation specific to high-current environments was stable across one generation. This study provides the first evidence of stability of DNA methylation patterns across one generation in an asexual animal. Together, our observations are consistent with the hypothesis that adaptive shell shape variation is at least in part determined by transgenerational plasticity, and that DNA methylation provides a potential mechanism for stability of shell shape across one generation.
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Dissertations / Theses on the topic "Generation"

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Malmaeus, Michelle, and Mehdi Hassan. "Generations Y:s förhållningssätt till arbetslivet : En studie om den nya generationen medarbetare." Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-28139.

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Syfte: Syftet med studien är att undersöka Generation Y:s förhållningssätt till arbetslivet för att ge en förståelse för deras föreställningar, behov och drivkrafter. Metod: Utgångspunkten är en kvalitativ studie, där fokusgrupper tillämpas för att att kartlägga och skapa förståelse för Generation Y:s förhållningssätt till arbetslivet. Dessa val ligger till grund för studiens utförande och tillvägagångssätt. Resultat & slutsats: Resultatet av fokusgruppsintervjuerna visade på generationens ifrågasättande attityd till arbetssätt och auktoriteter. Fokusgrupperna visade även på ett behov av att känna meningsfullhet i sitt arbete och la stor vikt vid trivsel och stabilitet på arbetsplatsen. Uppsatsens bidrag: Det empiriska bidraget är en ökad förståelse för Generation Y och deras förhållningssätt till arbetslivet, som ett medel att förbättra och utveckla relationer mellan ledare och unga medarbetare i dagens och framtidens organisationer.
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Sanhueza, Carolina, and Paula Falkevinge. "Generation Z : Den sökande generationen / Generation@ / Netgeneration." Thesis, Stockholms universitet, Institutionen för pedagogik och didaktik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-104751.

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Andledningen till att vi valt att undersöka Generation Z är för att vi som blivande studie- och yrkesvägledare kommer arbeta med dessa ungdomar. Generation Z är fortfarande unga men det hindrar dem inte från att vara uppkopplade på olika sociala medier. Aldrig tidigare har en generation haft så enkelt att hitta vad de söker efter. Den här generationen ligger i vårt intresse eftersom vi som framtida studie- och yrkesvägledare ska mer försöka knyta ihop säcken med all information som ges.
The reason that we have chosen to examine Generation Z is that we as a future career counselors will work with these young people. Generation Z is still young but it does not prevent them from being online on various social media. Never before has a generation have so easy to find what they are looking for. This generation is in our interest because as future career counselor will we try to tie together all the information provided.
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Henningsson, Anna. "Generating Generations - Automatic generation of 3D characters by simulation of genetic inheritance." Thesis, Umeå universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-128414.

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This thesis proposes a model that can simulate multiple generations of genetic inheritance as a way of creating varied 3D content automatically. The proposed model should be less complex and less resource demanding than existing models while producing considerably better results than a model generating content by simple randomization. To achieve variation, the design of the base 3D model for character customization was based on different animation techniques, modifiers were based on anthropometry elements from advanced genetic simulations and known cognitive factors important for recognition. By use of ideas from other successful content generation and other implementations of 3D genetics, four genetic inheritance models where designed, implemented and evaluated. The results show that the proposed model produces very good and advanced looking results of genetic kinship, and can be used for generation of large numbers of content and all while using small amounts of resources.
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Ronnfeldt, Jane. "Generational marketing: Baby boomers, Generation X and the net generation." CSUSB ScholarWorks, 2001. https://scholarworks.lib.csusb.edu/etd-project/2019.

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The purpose of this project is to gain a better understanding of the different market opportunities available to credit unions. The project differentiates the markets by age: Net Generation 2 to 22, Generation X 23 to 34 and the Baby Boomers 35 to 53. Each of these groups are important to the ongoing health of credit unions.
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Govitvatana, Wipanut Venique. "Generation gap in the workplace between Baby Boomers and Generation X." Online version, 2001. http://www.uwstout.edu/lib/thesis/2001/2001govitvatanaw.pdf.

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Wang, Zesen. "Generative Adversarial Networks in Text Generation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264575.

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The Generative Adversarial Network (GAN) was firstly proposed in 2014, and it has been highly studied and developed in recent years. It has obtained great success in the problems that cannot be explicitly defined by a math equation such as generating real images. However, since the GAN was initially designed to solve the problem in a continuous domain (image generation, for example), the performance of GAN in text generation is developing because the sentences are naturally discrete (no interpolation exists between “hello" and “bye"). In the thesis, it firstly introduces fundamental concepts in natural language processing, generative models, and reinforcement learning. For each part, some state-of-art methods and commonly used metrics are introduced. The thesis also proposes two models for the random sentence generation and the summary generation based on context, respectively. Both models involve the technique of the GAN and are trained on the large-scale dataset. Due to the limitation of resources, the model is designed and trained as a prototype. Therefore, it cannot achieve the state-of-art performance. However, the results still show the promising performance of the application of GAN in text generation. It also proposes a novel model-based metric to evaluate the quality of summary referring both the source text and the summary. The source code of the thesis will be available soon in the GitHub repository: https://github.com/WangZesen/Text-Generation-GAN.
Det generativa motståndsnätverket (GAN) introducerades först 2014 och det har studerats samt utvecklats starkt under senare år. GAN har uppnått stor framgång för problem som inte kan definieras uttryckligen av en matematisk ekvation, som att generera riktiga bilder. Men eftersom GAN ursprungligen var utformat för att lösa problemet i en kontinuerlig domän (till exempel bildgenerering), utvecklas GAN:s prestanda i textgenerering eftersom meningarna är naturligt diskreta (ingen interpolering finns mellan “hej" och “hejdå"). I examensarbetet introduceras grundläggande begrepp i naturlig språkbearbetning, generativa modeller och förstärkningslärande. För varje del introduceras några bästa tillgängliga metoder och vanligt förekommande mätvärden. Examensarbetet föreslår också två modeller för slumpmässig meningsgenerering respektive sammanfattningsgenerering baserat på sammanhang. Båda modellerna involverar tekniken för GAN och är tränade på storskaliga datamängder. På grund av begränsningen av resurser är modellen designad och tränad som en prototyp. Därför kan den inte heller uppnå bästa möjliga prestanda. Resultaten visar ändå lovande prestanda för tillämpningen av GAN i textgenerering. Den föreslår också en ny modellbaserad metrik för att utvärdera kvaliteten på sammanfattningen som hänvisar både till källtexten och sammanfattningen. Examensarbetets källkod kommer snart att finnas tillgänglig i GitHubförvaret: https://github.com/WangZesen/Text-Generation-GAN.
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Björkman, Lovisa, and Elin Myhrman. "Röster från tre generationer : En kvalitativ studie kring generationer på arbetsplatsen." Thesis, Malmö universitet, Malmö högskola, Institutionen för konst, kultur och kommunikation (K3), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-40089.

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Syftet med studien är att undersöka respondenter från Generation X (1964 – 1979), Y (1980 – 1995) och Zs (1996 – 2012) behov och önskemål på arbetsplatsen, vilka positiva och negativa attityder de har mot motivation, relation och arbetsmiljö. Studien har genomförts genom en kvalitativ undersökning, innehållsanalys i form av tematisering och genom att använda tidigare forskning kring generationer. Den kvalitativa metoden var intervjuer med 12 respondenter, varav fyra var från vardera generation. Modeller såsom Maslows behovstrappa, tvåfaktorteorin och förstärkningsteorin används i studien. Studien identifierade att ha kunskap kring komplexiteten kring generationsbaserade skillnaderna kan vara den avgörande faktorn för en framgångsrik organisation. På många vis finns det en konsensus mellan respondenterna men det finns även skillnader. Skillnaderna i attityder och behov mellan respondenterna har identifierats, som att respondenterna från Generation Z är drivna av inre motivation och arbetar mot ett syfte. De är driva för att skapa förändring i samhället genom deras arbete. Viktiga behov är familjära relationer och konstruktiv feedback. Respondenter från Generation Y drivs även de av ett syfte, men är även motiverade av yttre motivatorer som lön då de ser denna som ett erkännande för hårt arbete. Respondenter från Generation X däremot drivs mest av yttre motivation. Slutligen rekommenderar studien att organisationer skapar en kunskap kring områdets komplexitet för att förstå arbetarnas grundbehov kring arbetsplatsen. Om en förståelse skapas kan de utnyttja de anställdas kompetensen bättre och skapa framgångsrika team, för att främja en förberedelse för framtidens multigenerationella arbetsplats.
The purpose of the study is to research Generation X (1964 – 1979), Y (1980 – 1995) and Z´s (1996 – 2012) needs and wishes for the workplace, what they have for positive and negative attitudes towards motivation, relations and work environment. The study was conducted by conducting qualitative research, doing a content analysis, and using previous research of generations. The qualitative research was carried out by doing interviews with 12 persons, out of which four were from each generation. Models such as Maslow's hierarchy of needs, the Two-factor theory and Reinforcement theory has been used in the study. The study has found that having knowledge of the complexity of generational differences can be a deciding factor if an organisation is successful or not. In many ways there is a consensus between the generations, but there are also differences. Regarding the participants needs and attitudes it was identified that participants from Generation Z are very driven by inner motivation and working towards a purpose. They are driven to create change in society through their work. Important needs are familiar relationships and constructive feedback. Participants from Generation Y was also driven by a purpose but was also motivated by outer motivations such as a salary that can reinforce their hard work. Participants from Generation X were the most driven by outer motivations. In conclusion, the study recommends organisations to invest resources in the complexity of generational knowledge in the workplace to understand to understand the employees needs in the workplace. If an understanding is managed is it possible to make use of the employees’ competence better and create successful teams, as well to prepare the workplace for the future multigenerational workplace.
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El, Chababi Maria. "Are They Really Different? The Entrepreneurial Processes from the Perspective of Different Generations of Immigrant Entrepreneurs." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31692.

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Immigrant entrepreneurship is defined as business establishment and ownership among immigrants who arrive to a new host country. Immigrant entrepreneurship has become an important theme due to the increasing rates of immigration to developed countries, and its impact on their economic development. However it is also discredited and qualified as low value-added, rarely innovative, restricted to the ethnic communities and with stagnating growth potential. Following this debate, a new research stream affirms that immigrants should not be treated as one entity. Thus attention is shifting towards groups of immigrant entrepreneurs that were previously neglected in the literature. One such group consists of the second generation children of immigrants. Work to date provides ample investigation about immigrant entrepreneurs and their entrepreneurial process; however there is less research on similarities and differences in the entrepreneurial process experienced by first and second generation immigrant entrepreneurs. Despite some recent research on second generation immigrant entrepreneurs, this topic remains understudied. The objective of this study is to understand, from a multi-level perspective, how different generations of immigrant entrepreneurs experience the entrepreneurial process. Using a grounded theory approach and qualitative in-depth interviews, the findings indicate that first and second generation immigrant entrepreneurs experience the entrepreneurial process differently by facing different micro and macro level enablers and obstacles. They also recognize, evaluate and exploit opportunities differently. The extent to which they are embedded in specific environments affects their entrepreneurial experiences.
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Berman, Alan. "Generative adversarial networks for fine art generation." Master's thesis, University of Cape Town, 2020. http://hdl.handle.net/11427/32458.

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Generative Adversarial Networks (GANs), a generative modelling technique most commonly used for image generation, have recently been applied to the task of fine art generation. Wasserstein GANs and GANHack techniques have not been applied in GANs that generate fine art, despite their showing improved GAN results in other applications. This thesis investigates whether Wasserstein GANs and GANHack extensions to DCGANs can improve the quality of DCGAN-based fine art generation. There is also no accepted method of evaluating or comparing GANs for fine art generation. DCGAN's, Wasserstein GANs' and GANHack techniques' outputs on a modest computational budget were quantitatively and qualitatively compared to see which techniques showed improvement over DCGAN. A method for evaluating computer-generated fine art, HEART, is proposed to cover both the qualities of good human-created fine art and the shortcomings of computer-created fine art, and to include the cognitive and emotional impact as well as the visual appearance. Prominent GAN quantitative evaluation techniques were used to compare sample images these GANs produced on the MNIST, CIFAR-10 and Imagenet-1K image data sets. These results were compared with sample images these GANs produced on the above data sets, as well as on art data sets. A pilot study of HEART was performed with 20 users. Wasserstein GANs achieved higher visual quality outputs than the baseline DCGAN, as did the use of GANHacks, on all the fine art data sets and are thus recommended for use in future work on GAN-based fine art generation. The study also demonstrated that HEART can be used for the evaluation and comparison of art GANs, providing comprehensive, objective quality assessments which can be substantiated in terms of emotional and cognitive impact as well as visual appearance.
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Sorkhei, Moein. "Conditional Generative Flow for Street Scene Generation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288891.

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Generative modeling is a major branch of machine learning attributed to designing models that can learn how data are generated and hence are able to synthesize novel data. With the recent advancements in deep learning, generative models have been improved significantly and successfully applied in a variety of domains, including computer vision, video generation, audio generation, and even in medical applications. Amongst different categories of generative models, GAN-based models [1] are by far the most well-known generative models applied to a variety of computer vision tasks due to their ability in synthesizing large images. Recent advances in likelihood-based models [2, 3], however, suggest that these models could alternatively be used instead of GAN-based methods while exhibiting huge benefits such as stable training and useful learned representation. These advancements enable likelihood-based models to generate images that are as realistic as those of GAN-based models. In this project, we study modern generative models and their categorization with focus on computer vision tasks. We study Glow [2], a recent flow-based generative model, in detail and extend its architecture for conditional image generation tasks. We evaluate the model against the most popular GAN-based counterpart [4] and show that this model could be an alternative to GAN-based models while enjoying advantages that are inherently lacking in GANs. We also show that by generalizing the operations in Glow [2] so that they are all conditioned on the features of the condition input, we are able to generate more visually appealing results compared to recent Glow-based conditional models [5, 6].
Generativ modellering är en viktig gren av maskininlärning som innebär utformning av modeller som kan lära sig hur data genereras och därmed kan syn- tetisera nya data. Med de senaste framstegen inom djup inlärning har generati- va modeller förbättrats avsevärt och framgångsrikt tillämpats inom en mängd olika domäner, inklusive datorsyn, videogenerering, ljudgenerering och till och med inom medicinska tillämpningar. I detta projekt studerar vi moderna generativa modeller och deras kategorisering och undersöker deras tillämpning i datorsyn där vi använder villkor för att vägleda modellen för att generera bilder.
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Books on the topic "Generation"

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Generation 1989?: Zur Kritik einer populären Zeitdiagnose. Bielefeld: Transcript, 2012.

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O'Connor, Maureen. Generation to generation. London: Cassell, 1993.

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Sandra, Martz, and Coe Shirley 1956-, eds. Generation to generation. Watsonville, CA: Papier-Mache Press, 1998.

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O'Connor, Maureen. Generation to generation. London: Cassell in association with the LinkAge Trust, 1993.

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Ryan, Maureen. Generation to generation. London: Cassell Educational, 1992.

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Becker, H. A. De toekomst van de verloren generatie. Amsterdam: Meulenhoff, 1997.

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Leigh, Claire, and Joel Mullan. Generation crisis: Promoting justice between generations. London: Fabian Society, 2012.

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Merrell, James L. From generation to generation. St. Louis, Mo: Christian Board of Publication, 1992.

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Tomasov, Agnes. From generation to generation. [Toronto]: Azrieli Foundation, 2010.

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Mao, Xudong, and Qing Li. Generative Adversarial Networks for Image Generation. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6048-8.

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Book chapters on the topic "Generation"

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Kanz, Christine. "Generation — Generatio — Verwandtschaft." In Kleist-Jahrbuch 2010, 202–19. Stuttgart: J.B. Metzler, 2010. http://dx.doi.org/10.1007/978-3-476-00563-2_13.

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Einramhof-Florian, Helene. "Generation Corona, Generation Krise, Generation Lockdown, Generation Dauerkrise, Generation Aufbruch." In Fit für die jungen Generationen am Arbeitsplatz, 53–57. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-39149-2_5.

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Rabe-Kleberg, Ursula. "Von Generation zu Generation?" In Bildungsreform als Sozialreform, 77–88. Wiesbaden: VS Verlag für Sozialwissenschaften, 2005. http://dx.doi.org/10.1007/978-3-322-91642-6_6.

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Siritsky, Nachshon. "From Generation to Generation." In Reframing Trauma Through Social Justice, 180–99. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003379591-11.

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Mao, Xudong, and Qing Li. "GANs for Image Generation." In Generative Adversarial Networks for Image Generation, 9–52. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-6048-8_2.

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Arnold, Ludwig. "Generation." In Springer Monographs in Mathematics, 49–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-12878-7_2.

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Steinbach, Anja. "Generation." In Grundbegriffe der Soziologie, 129–31. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20978-0_26.

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Peck, Stewart B., Carol C. Mapes, Netta Dorchin, John B. Heppner, Eileen A. Buss, Gustavo Moya-Raygoza, Marjorie A. Hoy, et al. "Generation." In Encyclopedia of Entomology, 1589. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_1053.

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Toepfer, Georg. "Generation." In Encyclopedia of Renaissance Philosophy, 1–5. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-02848-4_259-1.

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Kočandrle, Radim, and Dirk L. Couprie. "Generation." In Apeiron, 73–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49754-9_7.

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Conference papers on the topic "Generation"

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Zhang, Boyu, Xin Wang, and Kai Liu. "Generative Model for Node Generation." In ICCAI '20: 2020 6th International Conference on Computing and Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3404555.3404599.

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Richards, Timothy, Edward K. Walters, J. Eliot B. Moss, Trek Palmer, and Charles C. Weems. "Towards universal code generator generation." In Distributed Processing Symposium (IPDPS). IEEE, 2008. http://dx.doi.org/10.1109/ipdps.2008.4536404.

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Tian, Yu, Xi Peng, Long Zhao, Shaoting Zhang, and Dimitris N. Metaxas. "CR-GAN: Learning Complete Representations for Multi-view Generation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/131.

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Generating multi-view images from a single-view input is an important yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn ?incomplete? representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network.We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning paths collaborate and compete in a parameter-sharing manner, yielding largely improved generality to ?unseen? dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. We evaluate our approach on a wide range of datasets. The results prove that CR-GAN significantly outperforms state-of-the-art methods, especially when generating from ?unseen? inputs in wild conditions.
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Maduskar, Advait, Aniket Ladukar, Shubhankar Gore, and Neha Patwari. "Music Generation using Deep Generative Modelling." In 2020 International Conference on Convergence to Digital World - Quo Vadis (ICCDW). IEEE, 2020. http://dx.doi.org/10.1109/iccdw45521.2020.9318683.

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Bhattacharjee, Rajat Kanti, Meghali Nandi, Amrit Jha, Gunajit Kalita, and Ferdous Ahmed Barbhuiya. "Handloom Design Generation Using Generative Networks." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9190925.

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Letizia, Nunzio Alexandro. "Identity Generation with Deep Generative Models." In ROSUS 2023 - Računalniška obdelava slik in njena uporaba v Sloveniji 2023. Univerza v Mariboru, Univerzitetna založba, 2023. http://dx.doi.org/10.18690/um.feri.4.2023.3.

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Deep generative models have drawn the attention of the AI community in the last decade. The scalability of neural architectures helps solving multiple relevant problems, e.g., text-to-image generation, otherwise not addressable. In the context of image data privacy, the increasing amount of produced, shared, and stored images imposes new measures to protect personal identity information. At the same time, such protection mechanisms need to preserve the image quality. In this talk, we discuss how PiktID is using some recent deep learning-based techniques for protecting human identities in pictures. We show several examples, and we present interesting use-cases.
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Liu, Dong, Yu Hong, Jianmin Yao, and Guodong Zhou. "Question Generation via Generative Adversarial Networks." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191871.

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Krause, Ben, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. "GeDi: Generative Discriminator Guided Sequence Generation." In Findings of the Association for Computational Linguistics: EMNLP 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-emnlp.424.

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Chen, Yijia. "Speech Generation by Generative Adversarial Network." In 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2021. http://dx.doi.org/10.1109/icbase53849.2021.00086.

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Stecanella, Priscilla A. J., Messias A. A. Faria, Elder G. Domingues, Pedro H. G. Gomes, Wesley P. Calixto, and Aylton J. Alves. "Eletricity generation using thermoelectric generator - TEG." In 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC). IEEE, 2015. http://dx.doi.org/10.1109/eeeic.2015.7165502.

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Reports on the topic "Generation"

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Adams, D. J., and S. G. Berenyi. Turbine-generator set development for power generation. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/656889.

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Yamashiro, Neil. Generation X. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada344977.

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David, Patty. Generation X Snapshots. AARP Research, December 2015. http://dx.doi.org/10.26419/res.00116.000.

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David, Patty. Generation X Demographics. AARP Research, December 2015. http://dx.doi.org/10.26419/res.00116.001.

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David, Patty. Generation X Finances. AARP Research, December 2015. http://dx.doi.org/10.26419/res.00116.002.

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David, Patty. Generation X Health. AARP Research, December 2015. http://dx.doi.org/10.26419/res.00116.003.

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David, Patty. Generation X Technology. AARP Research, December 2015. http://dx.doi.org/10.26419/res.00116.004.

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Zhao, Zilai, and Charles Gough. Next Generation Inverter. Office of Scientific and Technical Information (OSTI), April 2016. http://dx.doi.org/10.2172/1261839.

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Souers, P. C. New generation detonics. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/515977.

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Wilson, Ronald B., and Roger F. Sies. Next generation documents. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4830.

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