A Style-Based Generator Architecture for Generative Adversarial Networks

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.Comment: CVPR 2019 final versio

Turun kaupunginvaltuusto muutoksessa : Kassakaappisopimuksia ja avointa kabinettipolitiikkaa

Tutkielmassa käsitellään Turun kaupunginvaltuuston toimintakulttuuria valtuutettujen näkökulmasta vuodesta 2000 vuoteen 2019. Tutkimuksen tarkoitus oli myös kartoittaa vähäisesti tutkittua kunnallispolitiikan kenttää ja kerätä lähdemateriaalia kohdekaupungin päätöksentekotavoista. Tutkimus perustuu pääosin haastatteluihin, joihin osallistui noin 40 kaupunginvaltuutettua. Tutkimustulokset avasivat näkökulman murroksessa olevaan päätöksentekokoneistoon: päätöksenteon ajateltiin kehittyneen avoimempaan suuntaan, mutta toisaalta haastattelujen ajankohtana toimintatavoissa nähtiin paljon ongelmia. Tutkielmassa tarkastellaan lähemmin sopimiskulttuuria, valtuustoryhmien toimintaa sekä vuoden 2017 kaupunginjohtajan valintaa. Kaupunginvaltuustossa olevien puolueiden paikkamäärät olivat tutkimusjaksolla muuttuneet, ja muutosten seuraukset olivat niiden lukuarvoja suuremmat. Turun kunnallispolitiikka on valtakunnallisesti huonossa maineessa, ja tutkimuksessa havaittiin myös mahdollisia syitä tähän. Valtaosa tunnistetuista haasteista liittyi kuitenkin poliittiseen asetelmaan, eikä luottamushenkilöiden toimintaan. Kunnallispolitiikassa tapahtuu paljon epämuodollista vaikuttamista, jota ei aikaisemmin ole tutkittu. Tutkimus käsittelee valtuutettujen näkökulmia lähihistoriallisessa kontekstissa avaten ikkunan suomalaisen kuntademokratian toimintaan ja Turun lähihistoriaan.This study focuses on the municipal decision making in the city of Turku. For the purpose of the study around 40 local politicians and public servants were interviewed, all of which had held positions with the city of Turku within the time span: 2000-2019. The study examines local politics through three perspectives: The evolution of the political situation and inter-party negotiating practices during the years 2000-2019. The perspective of the council member on the decision making practices. And finally, a case study of the election of municipal manager in the year 2017. This master's thesis aims to find out how the day to day politics in Turku plays out, whether it has changed during the research period and what kind of challenges and strengths can be seen in the practices of different time periods and political parties

StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

Text-to-image synthesis has recently seen significant progress thanks to large pretrained language models, large-scale training data, and the introduction of scalable model families such as diffusion and autoregressive models. However, the best-performing models require iterative evaluation to generate a single sample. In contrast, generative adversarial networks (GANs) only need a single forward pass. They are thus much faster, but they currently remain far behind the state-of-the-art in large-scale text-to-image synthesis. This paper aims to identify the necessary steps to regain competitiveness. Our proposed model, StyleGAN-T, addresses the specific requirements of large-scale text-to-image synthesis, such as large capacity, stable training on diverse datasets, strong text alignment, and controllable variation vs. text alignment tradeoff. StyleGAN-T significantly improves over previous GANs and outperforms distilled diffusion models - the previous state-of-the-art in fast text-to-image synthesis - in terms of sample quality and speed.Comment: Project page: https://sites.google.com/view/stylegan-t

Playing location-based games is associated with psychological well-being: an empirical study of Pokémon GO players

Location-based games (LBGs) augment urban environments with virtual content turning them into a playground. The importance of understanding how different modes of play impact LBG players’ psychological well-being is emphasized by the enormous and constantly rising popularity of the genre. In this work, we use the two-factor theory of psychological well-being to investigate the associations between five constructs related to game mechanics and personality traits, and psychological well-being and fatigue. We test our proposed structural model with Finnish Pokémon GO players (N = 855). The results show deficient self-regulation and fear of missing out to be positively associated with gaming fatigue. Engagement with cooperative and individual game mechanics had a positive relationship with well-being. Competitive game mechanics were found to have a positive relationship with both well-being and fatigue. Finally, the overall playing intensity had a strong relationship with well-being, but no association with fatigue.</p

Semi-supervised semantic segmentation needs strong, varied perturbations

Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption - under which the data distribution consists of uniform class clusters of samples separated by low density regions - as important to its success. We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success. We then identify choice of augmentation as key to obtaining reliable performance without such low-density regions. We find that adapted variants of the recently proposed CutOut and CutMix augmentation techniques yield state-of-the-art semi-supervised semantic segmentation results in standard datasets. Furthermore, given its challenging nature we propose that semantic segmentation acts as an effective acid test for evaluating semi-supervised regularizers. Implementation at: https://github.com/Britefury/cutmix-semisup-seg.Comment: 21 pages, 7 figures, accepted to BMVC 202