尽管在构建强大的神经网络方面具有明显的计算优势，但使用单步方法的对抗训练（AT）是不稳定的，因为它遭受了灾难性的过度拟合（CO）：网络在对抗性训练的第一阶段获得了非平凡的鲁棒性，但突然达到了一个阶段在几次迭代中，他们很快失去了所有鲁棒性。尽管有些作品成功地预防了CO，但导致这种显着失败模式的不同机制仍然很少理解。但是，在这项工作中，我们发现数据结构与AT动力学之间的相互作用在CO中起着基本作用。特别是，通过对自然图像的典型数据集进行主动干预，我们建立了一个因果关系。在方法上单步中的数据和CO的发作。这种新的观点提供了对导致CO的机制的重要见解，并为更好地理解强大模型构建的一般动态铺平了道路。可以在https://github.com/gortizji/co_features上找到复制本文实验的代码。

最近，Wong等人。表明，使用单步FGSM的对抗训练导致一种名为灾难性过度拟合（CO）的特征故障模式，其中模型突然变得容易受到多步攻击的影响。他们表明，在FGSM（RS-FGSM）之前添加随机扰动似乎足以防止CO。但是，Andriushchenko和Flammarion观察到RS-FGSM仍会导致更大的扰动，并提出了一个昂贵的常规化器（Gradalign），DEMATER（GARGALIGN）DES昂贵（Gradalign）Dust Forrasiniger（Gradalign）Dust co避免在这项工作中，我们有条不紊地重新审视了噪声和剪辑在单步对抗训练中的作用。与以前的直觉相反，我们发现在干净的样品周围使用更强烈的噪声与不剪接相结合在避免使用大扰动半径的CO方面非常有效。基于这些观察结果，我们提出了噪声-FGSM（N-FGSM），尽管提供了单步对抗训练的好处，但在大型实验套件上没有经验分析，这表明N-FGSM能够匹配或超越以前的单步方法的性能，同时达到3 $ \ times $加速。代码可以在https://github.com/pdejorge/n-fgsm中找到

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.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community. Recent research efforts have quickly led to the design of novel algorithms able to reduce the impact of the catastrophic forgetting phenomenon in deep neural networks. Due to this surge of interest in the field, many competitions have been held in recent years, as they are an excellent opportunity to stimulate research in promising directions. This paper summarizes the ideas, design choices, rules, and results of the challenge held at the 3rd Continual Learning in Computer Vision (CLVision) Workshop at CVPR 2022. The focus of this competition is the complex continual object detection task, which is still underexplored in literature compared to classification tasks. The challenge is based on the challenge version of the novel EgoObjects dataset, a large-scale egocentric object dataset explicitly designed to benchmark continual learning algorithms for egocentric category-/instance-level object understanding, which covers more than 1k unique main objects and 250+ categories in around 100k video frames.

我们展示了一个端到端框架，以提高人造系统对不可预见的事件的弹性。该框架基于基于物理的数字双胞胎模型和三个负责实时故障诊断，预后和重新配置的模块。故障诊断模块使用基于模型的诊断算法来检测和分离断层，并在系统中产生干预措施，以消除不确定的诊断解决方案。我们通过使用基于物理学的数字双胞胎的平行化和替代模型来扩展故障诊断算法为所需的实时性能。预后模块跟踪故障进度，并训练在线退化模型，以计算系统组件的剩余使用寿命。此外，我们使用降解模型来评估断层进程对操作要求的影响。重新配置模块使用基于PDDL的计划，并带有语义附件来调整系统控件，从而最大程度地减少了对系统操作的故障影响。我们定义一个弹性度量，并以燃料系统模型的示例来说明该指标如何通过我们的框架改进。

腔内器官内部的导航是一项艰巨的任务，需要在操作员的手的运动与从内窥镜视频获得的信息之间进行非直觉的协调。开发自动化某些任务的工具可以减轻干预期间医生的身体和精神负担，从而使他们专注于诊断和决策任务。在本文中，我们提出了一种用于腔内导航的协同解决方案，该解决方案由3D打印的内窥镜软机器人组成，该机器人可以在腔内结构内安全移动。基于卷积神经网络（CNN）的Visual Servoing用于完成自主导航任务。 CNN经过幻象和体内数据训练以分割管腔，并提出了一种无模型的方法来控制受约束环境中的运动。在不同的路径配置中，在解剖幻像中验证了所提出的机器人。我们使用不同的指标分析机器人的运动，例如任务完成时间，平滑度，稳态中的误差以及均值和最大误差。我们表明，我们的方法适合在空心环境和条件下安全导航，这些环境和条件与最初对网络训练的条件不同。

强化学习（RL）通过原始像素成像和连续的控制任务在视频游戏中表现出了令人印象深刻的表现。但是，RL的性能较差，例如原始像素图像，例如原始像素图像。人们普遍认为，基于物理状态的RL策略（例如激光传感器测量值）比像素学习相比会产生更有效的样品结果。这项工作提出了一种新方法，该方法从深度地图估算中提取信息，以教授RL代理以执行无人机导航（UAV）的无地图导航。我们提出了深度模仿的对比度无监督的优先表示（DEPTH-CUPRL），该表示具有优先重播记忆的估算图像的深度。我们使用RL和对比度学习的组合，根据图像的RL问题引发。从无人驾驶汽车（UAV）对结果的分析中，可以得出结论，我们的深度cuprl方法在无MAP导航能力中对决策和优于最先进的像素的方法有效。

空气污染监测平台在预防和减轻污染影响方面发挥着非常重要的作用。绘图信号处理领域的最新进展使得可以使用图表描述和分析空气污染监测网络。其中一个主要应用是使用传感器的子集重新重建图表中的测量信号。使用来自传感器邻居的信息重建信号可以有助于提高网络数据的质量，示例是用相关的相邻节点的缺失数据填充，或者校正与更准确的相邻传感器的漂移传感器。本文比较了各种类型的图形信号重建方法应用于西班牙空气污染参考站的真实数据集。所考虑的方法是拉普拉斯插值，曲线​​图信号处理低通基的曲线曲线信号重建，以及基于内核的曲线图信号重建，并在测量O3，NO2和PM10的实际空气污染数据集上进行比较。示出了重建污染物信号的方法的能力，以及该重建的计算成本。结果表明了基于基于内核的曲线图信号重建的方法的优越性，以及具有大量低成本传感器的空气污染监测网络中的方法的难度。但是，我们表明可以通过简单的方法克服可扩展性，例如使用聚类算法对网络进行分区。

本文介绍了一种新型深度加强基于基于深度加强学习的3D Fapless导航系统（无人机）。我们提出了一个简单的学习系统，而不是使用一种简单的学习系统，该系统仅使用来自距离传感器的一些稀疏范围数据来训练学习代理。我们基于我们对两种最先进的双重评论家深度RL模型的方法：双延迟深度确定性政策梯度（TD3）和软演员 - 评论家（SAC）。我们表明，我们的两种方法可以基于深度确定性政策梯度（DDPG）技术和Bug2算法来胜过一种方法。此外，我们基于经常性神经网络（RNNS）的新的深度RL结构优于用于执行移动机器人的FAPLESS导航的当前结构。总体而言，我们得出结论，基于双重评论评价的深度RL方法与经常性神经网络（RNNS）更适合进行熔化的导航和避免无人机。