我们讨论集群分析的拓扑方面，并表明在聚类之前推断数据集的拓扑结构可以大大增强群集检测：理论论证和经验证据表明，聚类嵌入向量，代表数据歧管的结构，而不是观察到的特征矢量他们自己是非常有益的。为了证明，我们将流形学习方法与基于密度的聚类方法DBSCAN结合了歧管学习方法UMAP。合成和真实数据结果表明，这既简化和改善了多种低维问题，包括密度变化和/或纠缠形状的群集。我们的方法简化了聚类，因为拓扑预处理始终降低DBSCAN的参数灵敏度。然后，用dbscan聚类所得的嵌入可以超过诸如spectacl和clustergan之类的复杂方法。最后，我们的调查表明，聚类中的关键问题似乎不是数据的标称维度或其中包含多少不相关的功能，而是\ textIt {可分离}群集在环境观察空间中的\ textit {可分离}，它们嵌入了它们中。 ，通常是数据特征定义的（高维）欧几里得空间。我们的方法之所以成功，是因为我们将数据投影到更合适的空间后，从某种意义上说，我们执行了群集分析。

异常检测或异常检测是数据分析中的重要任务。我们从几何学角度讨论问题，并提供一个框架来利用数据集的度量结构。我们的方法基于多种假设，即，所观察到的名义上高维数据位于较低的维歧管上，并且可以通过多种学习方法来推断这种内在结构。我们表明，利用这种结构可显着改善高维数据中外围观测值的检测。我们还基于数据流形的几何形状和拓扑结构，在数学上精确，精确且在结构异常值之间进行了新颖的区别，这是一个新颖的，并且阐明了整个文献中普遍存在的概念模棱两可。我们的实验将功能数据集中在一类结构化的高维数据上，但是我们提出的框架是完全一般的，我们包括图像和图形数据应用程序。我们的结果表明，可以使用歧管学习方法检测和可视化高维和非尾数据的离群结构，并使用应用于歧管嵌入向量的标准离群评分方法进行量化。

诸如类风湿性关节炎的风湿性疾病的发病通常是亚临床的，这导致挑战疾病的早期检测。然而，可以使用诸如MRI或CT的成像技术来检测解剖结构的特征变化。现代成像技术，如化学交换饱和度转移（CEST）MRI驱动希望进一步通过体内代谢物的成像来改善早期检测。为了图像在患者的关节中的小结构，通常是由于疾病发生而导致的第一个区域之一，所以必须为CEST MR成像进行高分辨率。然而，目前，由于收购的潜在物理限制，CEST MR因其潜在的物理限制而受到固有的低分辨率。在这项工作中，我们将建立了基于神经网络的超分辨率方法的建立的上抽样技术。我们可以表明，神经网络能够从低分辨率到高分辨率不饱和CEST图像的映射显着优于当前方法。在测试设定的情况下，使用Reset神经网络可以实现32.29dB（+ 10％），NRMSE为0.14（+ 28％）的NRMSE，以及0.85（+ 15％）的SSSim，大大提高了基线。这项工作为超分辨率CEST MRI的神经网络预期调查铺平了道路，并且可能导致较早的风湿病发作的检测。

识别跨语言抄袭是挑战性的，特别是对于遥远的语言对和感知翻译。我们介绍了这项任务的新型多语言检索模型跨语言本体论（CL \ nobreakdash-osa）。 CL-OSA表示从开放知识图Wikidata获得的实体向量的文档。反对其他方法，Cl \ nobreakdash-osa不需要计算昂贵的机器翻译，也不需要使用可比较或平行语料库进行预培训。它可靠地歧义同音异义和缩放，以允许其应用于Web级文档集合。我们展示了CL-OSA优于从五个大局部多样化的测试语料中检索候选文档的最先进的方法，包括日语英语等遥控语言对。为了识别在角色级别的跨语言抄袭，CL-OSA主要改善了感觉识别翻译的检测。对于这些挑战性案例，CL-OSA在良好的Plagdet得分方面的表现超过了最佳竞争对手的比例超过两种。我们研究的代码和数据公开可用。

With the advent of Neural Style Transfer (NST), stylizing an image has become quite popular. A convenient way for extending stylization techniques to videos is by applying them on a per-frame basis. However, such per-frame application usually lacks temporal-consistency expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal-consistency suffers from one or more of the following drawbacks. They (1) are only suitable for a limited range of stylization techniques, (2) can only be applied in an offline fashion requiring the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency-control. Note that existing consistent video-filtering approaches aim to completely remove flickering artifacts and thus do not respect any specific consistency-control aspect. For stylization tasks, however, consistency-control is an essential requirement where a certain amount of flickering can add to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that can stylize video streams while providing interactive consistency-control. Apart from stylization, our approach also supports various other image processing filters. For achieving interactive performance, we develop a lite optical-flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. We show that the final consistent video-output using our flow network is comparable to that being obtained using state-of-the-art optical-flow network. Further, we employ an adaptive combination of local and global consistent features and enable interactive selection between the two. By objective and subjective evaluation, we show that our method is superior to state-of-the-art approaches.

Many problems in machine learning involve bilevel optimization (BLO), including hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems consist of two nested sub-problems, called the outer and inner problems, respectively. In practice, often at least one of these sub-problems is overparameterized. In this case, there are many ways to choose among optima that achieve equivalent objective values. Inspired by recent studies of the implicit bias induced by optimization algorithms in single-level optimization, we investigate the implicit bias of gradient-based algorithms for bilevel optimization. We delineate two standard BLO methods -- cold-start and warm-start -- and show that the converged solution or long-run behavior depends to a large degree on these and other algorithmic choices, such as the hypergradient approximation. We also show that the inner solutions obtained by warm-start BLO can encode a surprising amount of information about the outer objective, even when the outer parameters are low-dimensional. We believe that implicit bias deserves as central a role in the study of bilevel optimization as it has attained in the study of single-level neural net optimization.

By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models. However, previous methods do not explicitly control how much detail is synthesized, which results in a common criticism of these methods: users might be worried that a misleading reconstruction far from the input image is generated. In this work, we alleviate these concerns by training a decoder that can bridge the two regimes and navigate the distortion-realism trade-off. From a single compressed representation, the receiver can decide to either reconstruct a low mean squared error reconstruction that is close to the input, a realistic reconstruction with high perceptual quality, or anything in between. With our method, we set a new state-of-the-art in distortion-realism, pushing the frontier of achievable distortion-realism pairs, i.e., our method achieves better distortions at high realism and better realism at low distortion than ever before.

In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning scheme which removes the drawbacks of previous works, namely the strong dependency on co-modalities or additional refinement. These have been previously necessary to provide training signals for convergence. We formulate such a scheme as two sub-optimisation problems on texture learning and pose learning. We separately learn to predict realistic texture of objects from real image collections and learn pose estimation from pixel-perfect synthetic data. Combining these two capabilities allows then to synthesise photorealistic novel views to supervise the pose estimator with accurate geometry. To alleviate pose noise and segmentation imperfection present during the texture learning phase, we propose a surfel-based adversarial training loss together with texture regularisation from synthetic data. We demonstrate that the proposed approach significantly outperforms the recent state-of-the-art methods without ground-truth pose annotations and demonstrates substantial generalisation improvements towards unseen scenes. Remarkably, our scheme improves the adopted pose estimators substantially even when initialised with much inferior performance.

This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction to a different level of detail that is both consistent with the history of observed data and more effective in solving a given task.

Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems. As in any other growing subfield, patience seems to be a virtue since significant progress on integrating notions from both fields takes time, yet, major challenges such as the lack of ground truth benchmarks or a unified perspective on classical problems such as computer vision seem to hinder the momentum of the research movement. This present work exemplifies how the Pearl Causal Hierarchy (PCH) can be understood on image data by providing insights on several intricacies but also challenges that naturally arise when applying key concepts from Pearlian causality to the study of image data.