Probabilistic Image Colorization

We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous desirable properties. In particular, our model is able to produce multiple plausible and vivid colorizations for a given grayscale image and is one of the first colorization models to provide a proper stochastic sampling scheme. Moreover, our training procedure is supported by a rigorous theoretical framework that does not require any ad hoc heuristics and allows for efficient modeling and learning of the joint pixel color distribution. We demonstrate strong quantitative and qualitative experimental results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset

Scalarization for Multi-Task and Multi-Domain Learning at Scale

Training a single model on multiple input domains and/or output tasks allows for compressing information from multiple sources into a unified backbone hence improves model efficiency. It also enables potential positive knowledge transfer across tasks/domains, leading to improved accuracy and data-efficient training. However, optimizing such networks is a challenge, in particular due to discrepancies between the different tasks or domains: Despite several hypotheses and solutions proposed over the years, recent work has shown that uniform scalarization training, i.e., simply minimizing the average of the task losses, yields on-par performance with more costly SotA optimization methods. This raises the issue of how well we understand the training dynamics of multi-task and multi-domain networks. In this work, we first devise a large-scale unified analysis of multi-domain and multi-task learning to better understand the dynamics of scalarization across varied task/domain combinations and model sizes. Following these insights, we then propose to leverage population-based training to efficiently search for the optimal scalarization weights when dealing with a large number of tasks or domains.Comment: NeurIPS 2023; https://openreview.net/forum?id=TSuq3debn

Elevated Neopterin Levels Predict Fatal Outcome in SARS-CoV-2-Infected Patients

International audienceHighlights: Innate immune activation during Covid-19 infection is associated with pernicious clinical outcome.Background: Coronavirus disease 2019 (Covid-19) is a worldwide threat that has already caused more than 3 000 000 deaths. It is characterized by different patterns of disease evolution depending on host factors among which old-age and pre-existing comorbidities play a detrimental role. Previous coronavirus epidemics, notably SARS-CoV, were associated with increased serum neopterin levels, which can be interpreted as a sign of acute innate immunity in response to viral infection. Here we hypothesize that neopterin may serve as a biomarker of SARS-CoV-2 viral infection and Covid-19 disease severity.Methods: We measured neopterin blood levels by ELISA. Seric concentration was quantified from 256 healthy donors and 374 Covid-19 patients at hospital admission. Enrolled Covid-19 patients were all symptomatic and displayed a large spectrum of comorbidities. Patients were followed until disease resolution or death.Results: Severe and critically ill SARS-CoV-2 infected patients were characterized by a profound exacerbation of immune activation characterized by elevated neopterin blood levels. Systemic neopterin levels above 19nM stratified healthy individuals from Covid-19 patients with 87% specificity and 100% sensitivity. Moreover, systemic neopterin levels above 53nM differentiated non-survivors from survivors with 64% specificity and 100% sensitivity.Conclusion: We propose that neopterin concentration measured at arrival to hospital is a hallmark of severe Covid-19 and identifies a high-risk population of pernicious clinical outcome with a need for special medical care