A clustering termination procedure which is locally adaptive (with respect to the hierarchical tree of sets representative of the agglomerative merging) is proposed, for agglomerative hierarchical clustering on a set equipped with a distance function. It represents a multi-scale alternative to conventional scale dependent threshold based termination criteria.

The pandemic of these very recent years has led to a dramatic increase in people wearing protective masks in public venues. This poses obvious challenges to the pervasive use of face recognition technology that now is suffering a decline in performance. One way to address the problem is to revert to face recovery methods as a preprocessing step. Current approaches to face reconstruction and manipulation leverage the ability to model the face manifold, but tend to be generic. We introduce a method that is specific for the recovery of the face image from an image of the same individual wearing a mask. We do so by designing a specialized GAN inversion method, based on an appropriate set of losses for learning an unmasking encoder. With extensive experiments, we show that the approach is effective at unmasking face images. In addition, we also show that the identity information is preserved sufficiently well to improve face verification performance based on several face recognition benchmark datasets.

Differentiable Search Indices (DSIs) encode a corpus of documents in the parameters of a model and use the same model to map queries directly to relevant document identifiers. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents (+12\%). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting by a significant margin. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.

Large language models (LLMs) have shown impressive results across a variety of tasks while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial for both system developers and users in this setting. We propose and study Attributed QA as a key first step in the development of attributed LLMs. We develop a reproducable evaluation framework for the task, using human annotations as a gold standard and a correlated automatic metric that we show is suitable for development settings. We describe and benchmark a broad set of architectures for the task. Our contributions give some concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third key question (How to build LLMs with attribution?).

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i) unsupervised contrastive learning of normal representations of nodes in the target graph, and (ii) anomaly-aware one-class alignment that aligns these contrastive node representations and the representations of labelled normal nodes in the source graph, while enforcing significant deviation of the representations of the normal nodes from the labelled anomalous nodes in the source graph. In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions. Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. Code is available at https://github.com/QZ-WANG/ACT.

实例级图像检索（IIR）或简单的实例检索，涉及在数据集中查找包含查询实例（例如对象）的数据集中所有图像的问题。本文首次尝试使用基于实例歧视的对比学习（CL）解决此问题。尽管CL在许多计算机视觉任务中表现出令人印象深刻的性能，但在IIR领域也从未找到过类似的成功。在这项工作中，我们通过探索从预先训练和微调的CL模型中得出判别表示的能力来解决此问题。首先，我们通过比较预先训练的深度神经网络（DNN）分类器与CL模型学到的功能相比，研究了IIR转移学习的功效。这些发现启发了我们提出了一种新的培训策略，该策略通过使用平均精度（AP）损失以及微调方法来学习针对IIR量身定制的对比功能表示形式，从而优化CL以学习为导向IIR的功能。我们的经验评估表明，从挑战性的牛津和巴黎数据集中的预先培训的DNN分类器中学到的现成的特征上的表现显着提高。

夜间使用常规视觉摄像机运行的机器人由于噪声受限图像而在重建中面临重大挑战。先前的工作表明，爆发成像技术可用于部分克服这一问题。在本文中，我们开发了一种新型的功能检测器，该功能检测器直接在图像爆发上运行，从而在极低的光线条件下增强了基于视觉的重建。我们的方法通过在多尺度和多运动空间中共同搜索，在每次爆发中找到了定义明确的尺度和明显运动的关键点。因为我们在图像具有较高信噪比的阶段描述了这些功能，因此检测到的特征比常规嘈杂图像和突发的图像和表现出高度精确的最新特征更准确和匹配性能。我们显示了提高功能性能和摄像头姿势估计值，并在挑战光限制的场景中使用功能检测器展示了改进的结构，从而改善了结构。我们的功能Finder为在弱光方案和应用程序（包括夜间操作）中运行的机器人提供了重要的一步。

神经网络容易受到对抗性攻击的攻击：在其输入中添加精心设计，不可察觉的扰动可以改变其输出。对抗训练是针对此类攻击的训练强大模型的最有效方法之一。不幸的是，这种方法比神经网络的香草培训要慢得多，因为它需要在每次迭代时为整个培训数据构建对抗性示例。通过利用核心选择理论，我们展示了如何选择一小部分训练数据提供了一种原则性的方法来降低健壮训练的时间复杂性。为此，我们首先为对抗核心选择提供收敛保证。特别是，我们表明收敛界限直接与我们的核心在整个训练数据中计算出的梯度的距离如何。在我们的理论分析的激励下，我们建议使用此梯度近似误差作为对抗核心选择目标，以有效地减少训练集大小。建造后，我们在培训数据的这一子集上进行对抗训练。与现有方法不同，我们的方法可以适应各种培训目标，包括交易，$ \ ell_p $ -pgd和感知性对手培训。我们进行了广泛的实验，以证明我们的进近可以使对抗性训练加快2-3次，同时在清洁和稳健的精度中略有降解。

我们介绍了StreamNet，这是一种自动编码器体系结构，用于分析大量白质流线的高度异质几何形状。该提出的框架利用了Wasserstein-1度量的几何形状赋值特性，以实现整个流线束的直接编码和重建。我们表明，该模型不仅可以准确捕获人群中流线的分布结构，而且还能够在真实和合成流线之间实现出色的重建性能。使用最新的ART捆绑包比较度量标准，对40个健康对照的T1加权扩散成像产生的白质流线评估了实验模型性能。

大型语言模型已经证明了能够在自然语言和编程语言文本上进行条件和生成的能力。这样的模型打开了多语言代码生成的可能性：代码生成模型是否可以将知识从一种语言推广到另一种语言？尽管当代代码生成模型可以生成语义上正确的Python代码，但对它们使用其他语言的能力知之甚少。我们通过提出Multipl-E来促进该主题的探索，这是自然语言到代码生成的第一个多语言平行基准。 Multipl-E扩展了HumaneVal基准（Chen等，2021），以支持另外18种编程语言，涵盖了一系列编程范式和受欢迎程度。我们在Multipl-E：Codex和Incoder上评估了两个最先进的代码生成模型。我们发现，在几种语言上，法典匹配，甚至超过了其在Python上的性能。在多型E中表示的编程语言范围使我们能够探索语言频率和语言功能对模型性能的影响。最后，将代码生成基准分配给新编程语言的多重方法既可扩展又可扩展。我们描述了一种通用方法，可以轻松地增加对新基准和语言的支持。