7,711 research outputs found
Multi-view Self-supervised Disentanglement for General Image Denoising
With its significant performance improvements, the deep learning paradigm has become a standard tool for modern image denoisers. While promising performance has been shown on seen noise distributions, existing approaches often suffer from generalisation to unseen noise types or general and real noise. It is understandable as the model is designed to learn paired mapping (e.g. from a noisy image to its clean version). In this paper, we instead propose to learn to disentangle the noisy image, under the intuitive assumption that different corrupted versions of the same clean image share a common latent space. A self-supervised learning framework is proposed to achieve the goal, without looking at the latent clean image. By taking two different corrupted versions of the same image as input, the proposed Multi-view Self-supervised Disentanglement (MeD) approach learns to disentangle the latent clean features from the corruptions and recover the clean image consequently. Extensive experimental analysis on both synthetic and real noise shows the superiority of the proposed method over prior self-supervised approaches, especially on unseen novel noise types. On real noise, the proposed method even outperforms its supervised counterparts by over 3 dB
Multi-view Self-supervised Disentanglement for General Image Denoising
With its significant performance improvements, the deep learning paradigm has
become a standard tool for modern image denoisers. While promising performance
has been shown on seen noise distributions, existing approaches often suffer
from generalisation to unseen noise types or general and real noise. It is
understandable as the model is designed to learn paired mapping (e.g. from a
noisy image to its clean version). In this paper, we instead propose to learn
to disentangle the noisy image, under the intuitive assumption that different
corrupted versions of the same clean image share a common latent space. A
self-supervised learning framework is proposed to achieve the goal, without
looking at the latent clean image. By taking two different corrupted versions
of the same image as input, the proposed Multi-view Self-supervised
Disentanglement (MeD) approach learns to disentangle the latent clean features
from the corruptions and recover the clean image consequently. Extensive
experimental analysis on both synthetic and real noise shows the superiority of
the proposed method over prior self-supervised approaches, especially on unseen
novel noise types. On real noise, the proposed method even outperforms its
supervised counterparts by over 3 dB.Comment: International Conference on Computer Vision 2023 (ICCV 2023
An evolutionary algorithm with double-level archives for multiobjective optimization
Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problemlevel and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed
Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control
Automated anesthesia promises to enable more precise and personalized
anesthetic administration and free anesthesiologists from repetitive tasks,
allowing them to focus on the most critical aspects of a patient's surgical
care. Current research has typically focused on creating simulated environments
from which agents can learn. These approaches have demonstrated good
experimental results, but are still far from clinical application. In this
paper, Policy Constraint Q-Learning (PCQL), a data-driven reinforcement
learning algorithm for solving the problem of learning anesthesia strategies on
real clinical datasets, is proposed. Conservative Q-Learning was first
introduced to alleviate the problem of Q function overestimation in an offline
context. A policy constraint term is added to agent training to keep the policy
distribution of the agent and the anesthesiologist consistent to ensure safer
decisions made by the agent in anesthesia scenarios. The effectiveness of PCQL
was validated by extensive experiments on a real clinical anesthesia dataset.
Experimental results show that PCQL is predicted to achieve higher gains than
the baseline approach while maintaining good agreement with the reference dose
given by the anesthesiologist, using less total dose, and being more responsive
to the patient's vital signs. In addition, the confidence intervals of the
agent were investigated, which were able to cover most of the clinical
decisions of the anesthesiologist. Finally, an interpretable method, SHAP, was
used to analyze the contributing components of the model predictions to
increase the transparency of the model.Comment: 11 pages, 7 figure
Associations Between Home Dampness-related Indicators and Eczema among Preschool Children in Shanghai, China
AbstractIn recent years, prevalence of eczema has been increasing among preschool children in China. It's urgent to find associated factors. On basis of 13,335 questionnaires from parents or guardians for 4-6 year-old children (response rate 85.3%) in a cross-sectional study from April 2011 to April 2012 in Shanghai, the associations between home dampness related indicators and the prevalence of childhood eczema was investigated. There were 7.8%, 15.3%, 42.1%, 55.7%, and 30.7% of the surveyed residences who had visible mold spots, visible damp stains, damp clothing and/or bedding, water damage, condensation on window in winter, and moldy odor (six home dampness-related indicators), respectively. The prevalence of eczema (ever) and eczema (in the past 12 months) was 22.9% and 13.2%, respectively. These home dampness indicators had significant and strong associations with the increased risk of childhood eczema. With regard to the same indicators, the increased risk of eczema in girls was higher than in boys. Total numbers of home dampness-related indicators had notable dose-response relationships with the prevalence of childhood eczema. In conclusion, home dampness-related exposures are strong risk factors for childhood eczema
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