Learn the Time to Learn: Replay Scheduling in Continual Learning

Replay methods have shown to be successful in mitigating catastrophic forgetting in continual learning scenarios despite having limited access to historical data. However, storing historical data is cheap in many real-world applications, yet replaying all historical data would be prohibited due to processing time constraints. In such settings, we propose learning the time to learn for a continual learning system, in which we learn replay schedules over which tasks to replay at different time steps. To demonstrate the importance of learning the time to learn, we first use Monte Carlo tree search to find the proper replay schedule and show that it can outperform fixed scheduling policies in terms of continual learning performance. Moreover, to improve the scheduling efficiency itself, we propose to use reinforcement learning to learn the replay scheduling policies that can generalize to new continual learning scenarios without added computational cost. In our experiments, we show the advantages of learning the time to learn, which brings current continual learning research closer to real-world needs

Nerdery, Snobbery and Connoisseurship : Developing conceptual clarity within the area of refined consumption

As consumers in Western consumer culture have increasingly turned from high cultural to low cultural consumption categories to cultivate themselves, the meanings of the traditional and socio-cultural concepts used to represent different forms of consumer expertise have been blurred or altered. Drawing upon sociocultural literature on taste and distinction we attempt to provide theoretical clarity to the concepts of connoisseurship, snobbery, and nerdery; concepts that are often used interchangeably and without rigor in both (contemporary) popular and academic discourse. The outcome of our conceptual analysis is concretised using a semiotic square to illustrate how the concepts differ from each other. Our analysis suggests that the democratisation of consumption through the imprinting of status meanings upon traditionally illegitimate cultural objects may lead to the “bastardisation” of taste regarding those same illegitimate cultural categories – a performance formerly restricted to high culture

Fine-Grained and Continual Visual Recognition for Assisting Visually Impaired People

In recent years, computer vision-based assistive technologies have enabled visually impaired people to use automatic visual recognition on their mobile phones. These systems should be capable of recognizing objects on fine-grained levels to provide the user with accurate predictions. Additionally, the user should have the option to update the system continuously to recognize new objects of interest. However, there are several challenges that need to be tackled to enable such features with assistive vision systems in real and highly-varying environments. For instance, fine-grained image recognition usually requires large amounts of labeled data to be robust. Moreover, image classifiers struggle with retaining performance of previously learned abilities when they are adapted to new tasks. This thesis is divided into two parts where we address these challenges. First, we focus on the application of using assistive vision systems for grocery shopping, where items are naturally structured based on fine-grained details. We demonstrate how image classifiers can be trained with a combination of natural images and web-scraped information about the groceries to obtain more accurate classification performance compared to only using natural images for training. Thereafter, we bring forward a new approach for continual learning called replay scheduling, where we select which tasks to replay at different times to improve memory retention. Furthermore, we propose a novel framework for learning replay scheduling policies that can generalize to new continual learning scenarios for mitigating the catastrophic forgetting effect in image classifiers. This thesis provides insights on practical challenges that need to be addressed to enhance the usefulness of computer vision for assisting the visually impaired in real-world scenarios.De senaste åren har teknologiska hjälpmedel baserade på datorseende möjliggjort för synskadade personer att använda sig av automatisk visuell igenkänning på deras mobiltelefoner. Dessa system bör kunna känna igen objekt på finfördelade nivåer för att förse användaren med noggranna prediktioner. Användaren bör även ha möjligheten att uppdatera systemet kontinuerligt till att känna igen nya objekt av intresse. Dock finns det flera utmaningar som behöver avklaras för att aktivera dessa funktioner i synhjälpmedelssystem i reella och mycket varierande miljöer. Exempelvis behöver finfördelad bildigenkänning vanligtvis stora mängder märkt data för att vara robust. Dessutom har bildklassificerare besvär med att behålla sin prestanda av tidigare inlärda förmågor när de anpassas till nya uppgifter. Denna avhandling är uppdelad i två delar, där vi tar oss an dessa utmaningar. Först fokuserar vi på tillämpningen av att använda synhjälpmedelssystem för att handla matvaror, där varorna är naturligt strukturerade enligt finfördelade detaljer. Vi påvisar hur bildklassificerare kan tränas med en kombination av naturliga bilder och webbskrapad information om matvarorna för att erhålla mer träffsäker klassificeringsförmåga jämfört med att enbart använda naturliga bilder för träning. Därefter lägger vi fram ett nytt tillvägagångssätt för kontinuerlig inlärning som kallas replay scheduling (repris-schemaläggning), där vi väljer vilka uppgifter som ska repeteras vid olika tidpunkter för att förbättra bibehållande av minnen. Vi föreslår även ett nytt ramverk för inlärning av policyer för replay scheduling som kan generalisera till nya scenarion för kontinuerlig inlärning för att mildra effekten av katastrofal glömska i bildklassificerare. Denna avhandling ger insyn till praktiska utmaningar som behöver lösas för att förbättra användbarheten hos datorseende till att hjälpa synskadade personer i verkliga scenarier.QC 20221014</p

Fine-Grained and Continual Visual Recognition for Assisting Visually Impaired People

In recent years, computer vision-based assistive technologies have enabled visually impaired people to use automatic visual recognition on their mobile phones. These systems should be capable of recognizing objects on fine-grained levels to provide the user with accurate predictions. Additionally, the user should have the option to update the system continuously to recognize new objects of interest. However, there are several challenges that need to be tackled to enable such features with assistive vision systems in real and highly-varying environments. For instance, fine-grained image recognition usually requires large amounts of labeled data to be robust. Moreover, image classifiers struggle with retaining performance of previously learned abilities when they are adapted to new tasks. This thesis is divided into two parts where we address these challenges. First, we focus on the application of using assistive vision systems for grocery shopping, where items are naturally structured based on fine-grained details. We demonstrate how image classifiers can be trained with a combination of natural images and web-scraped information about the groceries to obtain more accurate classification performance compared to only using natural images for training. Thereafter, we bring forward a new approach for continual learning called replay scheduling, where we select which tasks to replay at different times to improve memory retention. Furthermore, we propose a novel framework for learning replay scheduling policies that can generalize to new continual learning scenarios for mitigating the catastrophic forgetting effect in image classifiers. This thesis provides insights on practical challenges that need to be addressed to enhance the usefulness of computer vision for assisting the visually impaired in real-world scenarios.De senaste åren har teknologiska hjälpmedel baserade på datorseende möjliggjort för synskadade personer att använda sig av automatisk visuell igenkänning på deras mobiltelefoner. Dessa system bör kunna känna igen objekt på finfördelade nivåer för att förse användaren med noggranna prediktioner. Användaren bör även ha möjligheten att uppdatera systemet kontinuerligt till att känna igen nya objekt av intresse. Dock finns det flera utmaningar som behöver avklaras för att aktivera dessa funktioner i synhjälpmedelssystem i reella och mycket varierande miljöer. Exempelvis behöver finfördelad bildigenkänning vanligtvis stora mängder märkt data för att vara robust. Dessutom har bildklassificerare besvär med att behålla sin prestanda av tidigare inlärda förmågor när de anpassas till nya uppgifter. Denna avhandling är uppdelad i två delar, där vi tar oss an dessa utmaningar. Först fokuserar vi på tillämpningen av att använda synhjälpmedelssystem för att handla matvaror, där varorna är naturligt strukturerade enligt finfördelade detaljer. Vi påvisar hur bildklassificerare kan tränas med en kombination av naturliga bilder och webbskrapad information om matvarorna för att erhålla mer träffsäker klassificeringsförmåga jämfört med att enbart använda naturliga bilder för träning. Därefter lägger vi fram ett nytt tillvägagångssätt för kontinuerlig inlärning som kallas replay scheduling (repris-schemaläggning), där vi väljer vilka uppgifter som ska repeteras vid olika tidpunkter för att förbättra bibehållande av minnen. Vi föreslår även ett nytt ramverk för inlärning av policyer för replay scheduling som kan generalisera till nya scenarion för kontinuerlig inlärning för att mildra effekten av katastrofal glömska i bildklassificerare. Denna avhandling ger insyn till praktiska utmaningar som behöver lösas för att förbättra användbarheten hos datorseende till att hjälpa synskadade personer i verkliga scenarier.QC 20221014</p

Conjugate-Prior-Regularized Multinomial pLSA for Collaborative Filtering

Collaborative filtering is a method for making predictions about consumer interests by collecting preferences or information about opinions from other consumers. For this purpose statistical modeling techniques are applied to learn personalized models for each consumer based on every purchase or provided rating to the available items. Such a technique is probabilistic Latent Semantic Analysis (pLSA), which within this thesis attempts to model consumers into groups based on similarities in movie preferences to improve personalized rating predictions on unseen movies. The main challenge with pLSA in collaborative filtering is the overfitting problem, which results in model parameters that are strictly determined by the past ratings and thus gives unreliable predictions for unknown data. To counteract the overfitting a regularization method called conjugate-prior-regularization is proposed to introduce additional information about the proportions of the model parameters. It is shown that the proposed regularization provides more robust learning from sparse datasets and also improves the recommendation performance on discrete ratings

Criteria for the Choice of EFL Resource Material at Primary Schools in Latvia

Diplomdarba tēma ir: “Kritēriji angļu valodas mācību materiāla izvēlē Latvijas pamatskolās.” Pētījumi mācību materiāla izvēles jomā ir nepietiekami, jo piedāvājums patstāvīgi mainās. Darbā pētītas reālas situācijas un vide. Darba mērķis – pētīt, teorētiski pamatot, aprakstīt un analizēt kritērijus mācību materiāla izvēlē Latvijas pamatskolās. Lai pierādītu izvirzīto hipotēzi, autore lieto sekojošas metodes – salīdzinošo literatūras analīzi, kas tiek iekļauta darba teorētiskajā daļā; aprakstošo metodi mācību grāmatu analizēšanai; anketu pētīšanu un rezultātu apkopošanu un mācību grāmatas “Opportunities” pre-intermediate lasīšanas un rakstīšanas uzdevumu vērtējumu. Veiktais pētījums liecina par to, ka lietotais materiāls veiksmīgai angļu valodas apgūšanai ir svarīgs gan skolēniem, gan skolotājiem. Darbā ir ietverta divu mācību grāmatu analīze: Michael Harris, David Mower un Anna Sikorzynska “Opportunities” pre-intermediate, un Kathy Gude un Michael Duckworth “Matrix” pre-intermediate. Latvijā nav izstrādāta vienota sistēma, pēc kuras skolotāji, kas pasniedz angļu valodu kā svešvalodu, var izvēlēties atbilstošu mācību materiālu. Analizējot skolotāju anketas var secināt, ka skolotāji strādā pēc dažādām grāmatām, un tās visas atbilst priekšmeta standartam. Skolēnu intereses un vajadzības mūsdienu sabiedrībā ir ļoti atšķirīgas, taču skolotāji anketās uzsver, ka jebkuram cilvēkam, kas ir speciālists savā jomā, ir jābūt spējīgam pielāgoties jebkurai situācijai, arī skolotājam vajadzētu prast atrast piemērotu, skolēnu vajadzībām un priekšmeta standartam atbilstošu materiālu, lai padziļinātu skolēnu zināšanas. Latvijā ir ļoti plašs mācību grāmatu klāsts un nav iespējama ideālas mācību grāmatas definīcija. Darbs sastāv no ievada, trīs nodaļām, secinājumiem, tēzēm, literatūras saraksta un sešiem pielikumiem.The theme of the present research is “Criteria for the choice of EFL resource material at primary schools in Latvia”. The previous studies in this area seem to be insufficient. The author of the research studies real situations and settings where the results of the investigation can be implemented. The study is important due to the achieved results. The research data was gathered by using a descriptive method, discourse analysis and two questionnaires for teachers and students of pre-intermediate level. The aim of the questionnaire given to the teachers was to obtain general information about their teaching practice and material used in the schools they work at. The paper includes the results obtained in the pilot study designed to evaluate reading and writing tasks provided in the coursebooks “Opportunities” and Matrix”. The present research shows that the selected material used during the English lessons is important for teachers and students. The paper analyses two coursebooks: Michael Harris, David Mower with Anna Sikorzynska “Opportunities” pre-intermediate, published by Longman in 2000 and Kathy Gude with Michael Duckworth “Matrix” pre-intermediate, published by Oxford University Press in 2002. It is recommended to use “Opportunities” as the background resource material to teach pre-intermediate level students. There is no definition of an ideal coursebook possible. During the process of writing the author of the research came to the conclusion that the choice of the coursebooks nowadays is wide and it depends on the teacher and the students which resource material they use to learn English as a foreign language. The paper consists of an introduction, three chapters, conclusions, theses, bibliography and 6 annexes

Masculinising domesticity: an investigation of men’s domestic foodwork

This study examines how men configure their gendered identity in relation to a traditionally feminised domain. Hegemonic masculinity is said to structure men’s dominance over women. We use the lens of hegemonic masculinity along with social fields of cultural production to understand new allocations of status capital in relation to gendered identity work. Sweden, a country permeated by an ideology of egalitarianism and having a history of high economic and symbolic incentives for the domestic field, has seemingly legitimised the domestic consumption field in the search for higher status. By exploring the transforming meanings of masculinity when men enter a traditionally feminine consumption domain in this particular cultural context, we identify how feminised masculinities are shaped into hegemonic masculinity. This in turn suggests that the currently most honoured way of being a man includes forms of masculinities that incorporate egalitarian relationships between men and women