Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in many tasks. Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data. However, traditional transfer learning methods often fail to generalize the pre-trained knowledge to the target task due to domain discrepancy. Self-supervised learning on graphs can increase the generalizability of graph features since self-supervision concentrates on inherent graph properties that are not limited to a particular supervised task. We propose a novel knowledge transfer strategy by integrating meta-learning with self-supervised learning to deal with the heterogeneity and scarcity of fMRI data. Specifically, we perform a self-supervised task on the source domain and apply meta-learning, which strongly improves the generalizability of the model using the bi-level optimization, to transfer the self-supervised knowledge to the target domain. Through experiments on a neurological disorder classification task, we demonstrate that the proposed strategy significantly improves target task performance by increasing the generalizability and transferability of graph-based knowledge.

大脑网络将大脑区域之间的复杂连接性描述为图形结构，这为研究脑连接素提供了强大的手段。近年来，图形神经网络已成为使用结构化数据的普遍学习范式。但是，由于数据获取的成本相对较高，大多数大脑网络数据集的样本量受到限制，这阻碍了足够的培训中的深度学习模型。受元学习的启发，该论文以有限的培训示例快速学习新概念，研究了在跨数据库中分析脑连接组的数据有效培训策略。具体而言，我们建议在大型样本大小的数据集上进行元训练模型，并将知识转移到小数据集中。此外，我们还探索了两种面向脑网络的设计，包括Atlas转换和自适应任务重新启动。与其他训练前策略相比，我们的基于元学习的方法实现了更高和稳定的性能，这证明了我们提出的解决方案的有效性。该框架还能够以数据驱动的方式获得有关数据集和疾病之间相似之处的新见解。

图形神经网络（GNNS）已成为图形结构化数据上许多应用的最先进的方法。 GNN是图形表示学习的框架，其中模型学习生成封装结构和特征相关信息的低维节点嵌入。 GNN通常以端到端的方式培训，导致高度专业化的节点嵌入。虽然这种方法在单任务设置中实现了很大的结果，但是可以用于执行多个任务的生成节点嵌入式（具有与单任务模型的性能）仍然是一个开放问题。我们提出了一种基于元学习的图形表示学习的新颖培训策略，这允许培训能够产生多任务节点嵌入的GNN模型。我们的方法避免了学习同时学习快速学习多个任务时产生的困难（即，具有梯度下降的几步），适应多个任务。我们表明，由我们的方法训练的模型生产的嵌入物可用于执行具有比单个任务和多任务端到端模型的可比性或令人惊讶的，甚至更高的性能的多个任务。

整合不同域的知识是人类学习的重要特征。学习范式如转移学习，元学习和多任务学习，通过利用新任务的先验知识，鼓励更快的学习和新任务的良好普遍来反映人类学习过程。本文提供了这些学习范例的详细视图以及比较分析。学习算法的弱点是另一个的力量，从而合并它们是文献中的一种普遍的特征。这项工作提供了对文章的文献综述，这些文章融合了两种算法来完成多个任务。这里还介绍了全球通用学习网络，在此介绍了元学习，转移学习和多任务学习的集合，以及一些开放的研究问题和未来研究的方向。

Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to target domains with unknown statistics. We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift. Further, we introduce two complementary losses which explicitly regularize the semantic structure of the feature space. Globally, we align a derived soft confusion matrix to preserve general knowledge about inter-class relationships. Locally, we promote domainindependent class-specific cohesion and separation of sample features with a metric-learning component. The effectiveness of our method is demonstrated with new state-of-the-art results on two common object recognition benchmarks. Our method also shows consistent improvement on a medical image segmentation task.

近年来，来自神经影像数据的脑疾病的单一受试者预测引起了人们的关注。然而，对于某些异质性疾病，例如严重抑郁症（MDD）和自闭症谱系障碍（ASD），大规模多站点数据集对预测模型的性能仍然很差。我们提出了一个两阶段的框架，以改善静止状态功能磁共振成像（RS-FMRI）的异质精神疾病的诊断。首先，我们建议对健康个体的数据进行自我监督的掩盖预测任务，以利用临床数据集中健康对照与患者之间的差异。接下来，我们在学习的判别性表示方面培训了一个有监督的分类器。为了建模RS-FMRI数据，我们开发Graph-S4；最近提出的状态空间模型S4扩展到图形设置，其中底层图结构未提前知道。我们表明，将框架和Graph-S4结合起来可以显着提高基于神经成像的MDD和ASD的基于神经影像学的单个主题预测模型和三个开源多中心RS-FMRI临床数据集的诊断性能。

视频异常检测旨在识别视频中发生的异常事件。由于异常事件相对较少，收集平衡数据集并培训二进制分类器以解决任务是不可行的。因此，最先前的方法只使用无监督或半监督方法从正常视频中学到。显然，它们是有限的捕获和利用鉴别异常特征，这导致受损的异常检测性能。在本文中，为了解决这个问题，我们通过充分利用用于视频异常检测的正常和异常视频来提出新的学习范式。特别是，我们制定了一个新的学习任务：跨域几次射击异常检测，可以从源域中的众多视频中学习知识，以帮助解决目标域中的几次异常检测。具体而言，我们利用目标普通视频的自我监督培训来减少域间隙，并设计一个Meta Context Cenception模块，以探索几次拍摄设置中的事件的视频上下文。我们的实验表明，我们的方法显着优于DotA和UCF犯罪数据集的基线方法，新任务有助于更实用的异常检测范例。

基于元学习的现有方法通过从（源域）基础类别的培训任务中学到的元知识来预测（目标域）测试任务的新颖类标签。但是，由于范围内可能存在较大的域差异，大多数现有作品可能无法推广到新颖的类别。为了解决这个问题，我们提出了一种新颖的对抗特征增强（AFA）方法，以弥合域间隙，以几乎没有学习。该特征增强旨在通过最大化域差异来模拟分布变化。在对抗训练期间，通过将增强特征（看不见的域）与原始域（可见域）区分开来学习域歧视器，而将域差异最小化以获得最佳特征编码器。所提出的方法是一个插件模块，可以轻松地基于元学习的方式将其集成到现有的几种学习方法中。在九个数据集上进行的广泛实验证明了我们方法对跨域几乎没有射击分类的优越性，与最新技术相比。代码可从https://github.com/youthhoo/afa_for_few_shot_learning获得

最近，深度学习方法在交通预测方面取得了长足的进步，但它们的性能取决于大量的历史数据。实际上，我们可能会面临数据稀缺问题。在这种情况下，深度学习模型无法获得令人满意的性能。转移学习是解决数据稀缺问题的一种有前途的方法。但是，流量预测中现有的转移学习方法主要基于常规网格数据，这不适用于流量网络中固有的图形数据。此外，现有的基于图的模型只能在道路网络中捕获共享的流量模式，以及如何学习节点特定模式也是一个挑战。在本文中，我们提出了一种新颖的传输学习方法来解决流量预测，几乎可以将知识从数据富的源域转移到数据范围的目标域。首先，提出了一个空间图形神经网络，该网络可以捕获不同道路网络的节点特异性时空交通模式。然后，为了提高转移的鲁棒性，我们设计了一种基于模式的转移策略，我们利用基于聚类的机制来提炼源域中的常见时空模式，并使用这些知识进一步提高了预测性能目标域。现实世界数据集的实验验证了我们方法的有效性。

图形神经网络（GNN），图数据上深度神经网络的概括已被广泛用于各个领域，从药物发现到推荐系统。但是，当可用样本很少的情况下，这些应用程序的GNN是有限的。元学习一直是解决机器学习中缺乏样品的重要框架，近年来，研究人员已经开始将元学习应用于GNNS。在这项工作中，我们提供了对涉及GNN的不同元学习方法的综合调查，这些方法在各种图表中显示出使用这两种方法的力量。我们根据提出的架构，共享表示和应用程序分类文献。最后，我们讨论了几个激动人心的未来研究方向和打开问题。

Purpose: Surgery scene understanding with tool-tissue interaction recognition and automatic report generation can play an important role in intra-operative guidance, decision-making and postoperative analysis in robotic surgery. However, domain shifts between different surgeries with inter and intra-patient variation and novel instruments' appearance degrade the performance of model prediction. Moreover, it requires output from multiple models, which can be computationally expensive and affect real-time performance. Methodology: A multi-task learning (MTL) model is proposed for surgical report generation and tool-tissue interaction prediction that deals with domain shift problems. The model forms of shared feature extractor, mesh-transformer branch for captioning and graph attention branch for tool-tissue interaction prediction. The shared feature extractor employs class incremental contrastive learning (CICL) to tackle intensity shift and novel class appearance in the target domain. We design Laplacian of Gaussian (LoG) based curriculum learning into both shared and task-specific branches to enhance model learning. We incorporate a task-aware asynchronous MTL optimization technique to fine-tune the shared weights and converge both tasks optimally. Results: The proposed MTL model trained using task-aware optimization and fine-tuning techniques reported a balanced performance (BLEU score of 0.4049 for scene captioning and accuracy of 0.3508 for interaction detection) for both tasks on the target domain and performed on-par with single-task models in domain adaptation. Conclusion: The proposed multi-task model was able to adapt to domain shifts, incorporate novel instruments in the target domain, and perform tool-tissue interaction detection and report generation on par with single-task models.

Wearable sensor-based human activity recognition (HAR) has emerged as a principal research area and is utilized in a variety of applications. Recently, deep learning-based methods have achieved significant improvement in the HAR field with the development of human-computer interaction applications. However, they are limited to operating in a local neighborhood in the process of a standard convolution neural network, and correlations between different sensors on body positions are ignored. In addition, they still face significant challenging problems with performance degradation due to large gaps in the distribution of training and test data, and behavioral differences between subjects. In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features. The proposed method is capable of learning cross-domain embedding feature representations from multiple subjects datasets using adversarial learning and the maximum mean discrepancy (MMD) regularization to align the data distribution over multiple domains. In the proposed method, we adopt the teacher-free self-knowledge distillation to improve the stability of the training procedure and the performance of human activity recognition. Experimental results show that TASKED not only outperforms state-of-the-art methods on the four real-world public HAR datasets (alone or combined) but also improves the subject generalization effectively.

很少有射击分类旨在仅使用几个标签示例就可以很好地学习新对象类别。从其他模型转移功能表示是一种流行的方法，用于解决几乎没有射击的分类问题。在这项工作中，我们对各种功能表示形式进行了系统的研究，以进行几次射击分类，包括从MAML中学到的表示，监督分类和几个常见的自我监督任务。我们发现，从更复杂的任务中学习倾向于为几个射击分类提供更好的表示，因此我们建议使用从多个任务中学到的表示形式进行几次分类。加上功能选择和投票以处理小样本量的新技巧，我们的直接转移学习方法提供的性能可与几个基准数据集上的最先进相提并论。

最近对基于细粒的基于草图的图像检索（FG-SBIR）的重点已转向将模型概括为新类别，而没有任何培训数据。但是，在现实世界中，经过训练的FG-SBIR模型通常应用于新类别和不同的人类素描器，即不同的绘图样式。尽管这使概括问题复杂化，但幸运的是，通常可以使用一些示例，从而使模型适应新的类别/样式。在本文中，我们提供了一种新颖的视角 - 我们没有要求使用概括的模型，而是提倡快速适应的模型，在测试过程中只有很少的样本（以几种方式）。为了解决这个新问题，我们介绍了一种基于几个关键修改的基于新型的模型 - 静态元学习（MAML）框架：（1）作为基于边缘的对比度损失的检索任务，我们简化了内部循环中的MAML训练使其更稳定和易于处理。 （2）我们的对比度损失的边距也通过其余模型进行了元学习。 （3）在外循环中引入了另外三个正规化损失，以使元学习的FG-SBIR模型对类别/样式适应更有效。在公共数据集上进行的广泛实验表明，基于概括和基于零射的方法的增益很大，还有一些强大的射击基线。

Given a resource-rich source graph and a resource-scarce target graph, how can we effectively transfer knowledge across graphs and ensure a good generalization performance? In many high-impact domains (e.g., brain networks and molecular graphs), collecting and annotating data is prohibitively expensive and time-consuming, which makes domain adaptation an attractive option to alleviate the label scarcity issue. In light of this, the state-of-the-art methods focus on deriving domain-invariant graph representation that minimizes the domain discrepancy. However, it has recently been shown that a small domain discrepancy loss may not always guarantee a good generalization performance, especially in the presence of disparate graph structures and label distribution shifts. In this paper, we present TRANSNET, a generic learning framework for augmenting knowledge transfer across graphs. In particular, we introduce a novel notion named trinity signal that can naturally formulate various graph signals at different granularity (e.g., node attributes, edges, and subgraphs). With that, we further propose a domain unification module together with a trinity-signal mixup scheme to jointly minimize the domain discrepancy and augment the knowledge transfer across graphs. Finally, comprehensive empirical results show that TRANSNET outperforms all existing approaches on seven benchmark datasets by a significant margin.

Few-shot learning aims to fast adapt a deep model from a few examples. While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift. We thus propose the Omni-Training framework to seamlessly bridge pre-training and meta-training for data-efficient few-shot learning. Our first contribution is a tri-flow Omni-Net architecture. Besides the joint representation flow, Omni-Net introduces two parallel flows for pre-training and meta-training, responsible for improving domain transferability and task transferability respectively. Omni-Net further coordinates the parallel flows by routing their representations via the joint-flow, enabling knowledge transfer across flows. Our second contribution is the Omni-Loss, which introduces a self-distillation strategy separately on the pre-training and meta-training objectives for boosting knowledge transfer throughout different training stages. Omni-Training is a general framework to accommodate many existing algorithms. Evaluations justify that our single framework consistently and clearly outperforms the individual state-of-the-art methods on both cross-task and cross-domain settings in a variety of classification, regression and reinforcement learning problems.

图形存在于许多现实世界中的应用中，例如财务欺诈检测，商业建议和社交网络分析。但是，鉴于图形注释或标记的高成本，我们面临严重的图形标签 - 刻度问题，即，图可能具有一些标记的节点。这样一个问题的一个例子是所谓的\ textit {少数弹性节点分类}。该问题的主要方法均依靠\ textit {情节元学习}。在这项工作中，我们通过提出一个基本问题来挑战现状，元学习是否是对几个弹性节点分类任务的必要条件。我们在标准的几杆节点分类设置下提出了一个新的简单框架，作为学习有效图形编码器的元学习的替代方法。该框架由有监督的图形对比学习以及新颖的数据增强，子图编码和图形上的多尺度对比度组成。在三个基准数据集（Corafull，Reddit，OGBN）上进行的广泛实验表明，新框架显着胜过基于最先进的元学习方法。

Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target domain. The key to addressing CD-FSL is to narrow the domain gap and transferring knowledge of a network trained on the source domain to the target domain. To help knowledge transfer, this paper introduces an intermediate domain generated by mixing images in the source and the target domain. Specifically, to generate the optimal intermediate domain for different target data, we propose a novel target guided dynamic mixup (TGDM) framework that leverages the target data to guide the generation of mixed images via dynamic mixup. The proposed TGDM framework contains a Mixup-3T network for learning classifiers and a dynamic ratio generation network (DRGN) for learning the optimal mix ratio. To better transfer the knowledge, the proposed Mixup-3T network contains three branches with shared parameters for classifying classes in the source domain, target domain, and intermediate domain. To generate the optimal intermediate domain, the DRGN learns to generate an optimal mix ratio according to the performance on auxiliary target data. Then, the whole TGDM framework is trained via bi-level meta-learning so that TGDM can rectify itself to achieve optimal performance on target data. Extensive experimental results on several benchmark datasets verify the effectiveness of our method.

图形神经网络（GNN）是通过学习通用节点表示形式来建模和处理图形结构数据的主要范例。传统的培训方式GNNS取决于许多标记的数据，这导致了成本和时间的高需求。在某个特殊场景中，它甚至不可用。可以通过图形结构数据本身生成标签的自我监督表示学习是解决此问题的潜在方法。并且要研究对异质图的自学学习问题的研究比处理同质图更具挑战性，对此，研究也更少。在本文中，我们通过基于Metapath（SESIM）的结构信息提出了一种用于异质图的自我监督学习方法。提出的模型可以通过预测每个Metapath中节点之间的跳跃数来构建借口任务，以提高主任务的表示能力。为了预测跳跃数量，Sesim使用数据本身来生成标签，避免了耗时的手动标签。此外，预测每个Metapath中的跳跃数量可以有效地利用图形结构信息，这是节点之间的重要属性。因此，Sesim加深对图形结构模型的理解。最后，我们共同培训主要任务和借口任务，并使用元学习来平衡借口任务对主要任务的贡献。经验结果验证了SESIM方法的性能，并证明该方法可以提高传统神经网络在链接预测任务和节点分类任务上的表示能力。

在这里，我们提出了一种用于多模式神经影像融合学习（HGM）的异质图形神经网络。传统的基于GNN的模型通常假设大脑网络是具有单一类型节点和边缘的均匀图形。然而，巨大的文献已经显示出人脑的异质性，特别是在两个半球之间。均匀脑网络不足以模拟复杂的脑状态。因此，在这项工作中，我们首先用多型节点（即左右半球节点）和多型边缘（即半球形边缘）来模拟大脑网络作为异质图。此外，我们还提出了一种基于Hetergoneou Brain网络的自我监督的预训练策略，以解决由于复杂的模型和小样本大小而过度的问题。我们在两个数据集合的结果显示出拟议模型的优越性，以疾病预测任务的其他多模型方法。此外，消融实验表明，我们具有预训练策略的模型可以减轻训练样本大小有限的问题。