在过去的十年中,我们看到了工业数据,计算能力的巨大改善以及机器学习的重大理论进步。这为在大规模非线性监控和控制问题上使用现代机器学习工具提供了机会。本文对过程行业的应用进行了对最新结果的调查。
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本文提出了一类新的实时优化方案,以克服不确定过程的系统模型不匹配。这项工作的新颖性在于在贝叶斯优化框架内集成无衍生优化的优化方案和多保真高斯进程。所提出的方案对随机系统进行了两个高斯过程,通过测量来模拟(已知)过程模型,另一个,真实系统。以这种方式,可以通过模型获得低保真度样本,而通过系统的测量获得高保真样本。该框架在非参数时捕获系统的行为,同时通过采集函数驾驶探索。使用高斯进程代表系统的好处是能够实时地执行不确定性量化,并允许有机会限制以满足高信任。这导致一种实用的方法,其在数值案例研究中示出,包括半批量光生物反应器优化问题。
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This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesirable distortions on identity features, which is not always favorable in id-sensitive ReID tasks. In this paper, we propose to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning. A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. Deviating from previous GAN-based ReID methods that only work in id-unrelated space (pose and camera style), we conduct GAN-based augmentation on both id-unrelated and id-related features. We further propose specific contrastive losses to help our network learn invariance from id-unrelated and id-related augmentations. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.
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Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based classification to improve bladder tissue classification when annotations are limited in multi-domain data.
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Large Neighborhood Search (LNS) is a popular heuristic algorithm for solving combinatorial optimization problems (COP). It starts with an initial solution to the problem and iteratively improves it by searching a large neighborhood around the current best solution. LNS relies on heuristics to select neighborhoods to search in. In this paper, we focus on designing effective and efficient heuristics in LNS for integer linear programs (ILP) since a wide range of COPs can be represented as ILPs. Local Branching (LB) is a heuristic that selects the neighborhood that leads to the largest improvement over the current solution in each iteration of LNS. LB is often slow since it needs to solve an ILP of the same size as input. Our proposed heuristics, LB-RELAX and its variants, use the linear programming relaxation of LB to select neighborhoods. Empirically, LB-RELAX and its variants compute as effective neighborhoods as LB but run faster. They achieve state-of-the-art anytime performance on several ILP benchmarks.
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Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to biases. We first present a margin-based theoretical framework that allows us to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when dealing with biased data. Based on that, we derive a novel formulation of the supervised contrastive loss (epsilon-SupInfoNCE), providing more accurate control of the minimal distance between positive and negative samples. Furthermore, thanks to our theoretical framework, we also propose FairKL, a new debiasing regularization loss, that works well even with extremely biased data. We validate the proposed losses on standard vision datasets including CIFAR10, CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with epsilon-SupInfoNCE, reaching state-of-the-art performance on a number of biased datasets, including real instances of biases in the wild.
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在许多情况下,更简单的模型比更复杂的模型更可取,并且该模型复杂性的控制是机器学习中许多方法的目标,例如正则化,高参数调整和体系结构设计。在深度学习中,很难理解复杂性控制的潜在机制,因为许多传统措施并不适合深度神经网络。在这里,我们开发了几何复杂性的概念,该概念是使用离散的dirichlet能量计算的模型函数变异性的量度。使用理论论据和经验结果的结合,我们表明,许多常见的训练启发式方法,例如参数规范正规化,光谱规范正则化,平稳性正则化,隐式梯度正则化,噪声正则化和参数初始化的选择,都可以控制几何学复杂性,并提供一个统一的框架,以表征深度学习模型的行为。
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自动分割前庭造型瘤(VS)和来自磁共振成像(MRI)的耳蜗可以促进与治疗计划。无监督的分割方法已显示出令人鼓舞的结果,而无需耗时且费力的手动标记过程。在本文中,我们提出了一种在无监督域的适应设置中进行VS和耳蜗分割的方法。具体而言,我们首先开发了跨站点的跨模式未配对的图像翻译策略,以丰富合成数据的多样性。然后,我们设计了一种基于规则的离线增强技术,以进一步最大程度地减少域间隙。最后,我们采用一个自我训练的自我配置分割框架,以获得最终结果。在Crossmoda 2022验证排行榜上,我们的方法已获得竞争性与耳蜗细分性能,平均骰子得分为0.8178 $ \ pm $ 0.0803和0.8433 $ \ pm $ 0.0293。
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模拟逼真的传感器是自主系统数据生成的挑战,通常涉及精心手工的传感器设计,场景属性和物理建模。为了减轻这一点,我们引入了一条管道,用于对逼真的激光雷达传感器进行数据驱动的模拟。我们提出了一个模型,该模型可以在RGB图像和相应的LIDAR功能(例如Raydrop或每点强度)之间直接从真实数据集中进行映射。我们表明,我们的模型可以学会编码逼真的效果,例如透明表面上的掉落点或反射材料上的高强度回报。当应用于现成的模拟器软件提供的天真播放点云时,我们的模型通过根据场景的外观预测强度和删除点来增强数据,以匹配真实的激光雷达传感器。我们使用我们的技术来学习两个不同的LIDAR传感器的模型,并使用它们相应地改善模拟的LiDAR数据。通过车辆细分的示例任务,我们表明通过我们的技术增强模拟点云可以改善下游任务性能。
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卷积神经网络(CNN)已通过卷积和汇总实现了图像分类的重大进展。特别是,图像池将连接的离散网格转换为具有相同连接性的还原网格,并允许还原功能考虑图像的所有像素。但是,对于图形而不存在满足此类属性的合并。实际上,某些方法基于一个顶点选择步骤,该步骤会导致重要信息丢失。其他方法学习了顶点集的模糊聚类,该聚类几乎诱导了几乎完全减少的图形。我们建议使用名为MivSpool的新合并方法克服这两个问题。该方法基于使用最大独立顶点集(MIV)和将其余顶点分配给幸存者的最大独立顶点集(MIV)的选择的顶点。因此,我们的方法不会丢弃任何顶点信息,也不会人为地增加图的密度。实验结果表明,各种标准数据集上的图形分类的精度有所提高。
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