Full-Glow: Fully conditional Glow for more realistic image generation

Autonomous agents, such as driverless cars, require large amounts of labeled visual data for their training. A viable approach for acquiring such data is training a generative model with collected real data, and then augmenting the collected real dataset with synthetic images from the model, generated with control of the scene layout and ground truth labeling. In this paper we propose Full-Glow, a fully conditional Glow-based architecture for generating plausible and realistic images of novel street scenes given a semantic segmentation map indicating the scene layout. Benchmark comparisons show our model to outperform recent works in terms of the semantic segmentation performance of a pretrained PSPNet. This indicates that images from our model are, to a higher degree than from other models, similar to real images of the same kinds of scenes and objects, making them suitable as training data for a visual semantic segmentation or object recognition system.Comment: 17 pages, 12 figure

Användning av villkorad Glow för gatubildsgenerering och andra villkorade bildgenereringsuppgifter

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

Användning av villkorad Glow för gatubildsgenerering och andra villkorade bildgenereringsuppgifter

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

[Re] Unsupervised Scalable Representation Learning for Multivariate Time Series

Replication in NeurIPS 2019 Reproducibility Challenge (Python), QC 20210710</p

CSAW-M : An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers – without being explicitly trained for these tasks – than its breast density counterparts.QC 20231218</p