79 research outputs found
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
- …