While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset. Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. The core of our approach is a measurement Temporal Output Discrepancy (TOD) that estimates the sample loss by evaluating the discrepancy of outputs given by models at different optimization steps. Our theoretical investigation shows that TOD lower-bounds the accumulated sample loss thus it can be used to select informative unlabeled samples. On basis of TOD, we further develop an effective unlabeled data sampling strategy as well as an unsupervised learning criterion for active learning. Due to the simplicity of TOD, our methods are efficient, flexible, and task-agnostic. Extensive experimental results demonstrate that our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks. In addition, we show that TOD can be utilized to select the best model of potentially the highest testing accuracy from a pool of candidate models.

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.

近年来，基于深度学习的模型在视频超分辨率（VSR）方面取得了显着性能，但是这些模型中的大多数不适用于在线视频应用程序。这些方法仅考虑失真质量，而忽略了在线应用程序的关键要求，例如低延迟和模型较低的复杂性。在本文中，我们专注于在线视频传输，其中需要VSR算法来实时生成高分辨率的视频序列。为了应对此类挑战，我们提出了一种基于一种新的内核知识转移方法，称为卷积核旁路移植物（CKBG）。首先，我们设计了一个轻巧的网络结构，该结构不需要将来的帧作为输入，并节省了缓存这些帧的额外时间成本。然后，我们提出的CKBG方法通过用``核移植物）''绕过原始网络来增强这种轻巧的基础模型，这些网络是包含外部预验证图像SR模型的先验知识的额外卷积内核。在测试阶段，我们通过将其转换为简单的单路结构来进一步加速移植的多支球网络。实验结果表明，我们提出的方法可以处理高达110 fps的在线视频序列，并且模型复杂性非常低和竞争性SR性能。

很少有人提出了几乎没有阶级的课程学习（FSCIL），目的是使深度学习系统能够逐步学习有限的数据。最近，一位先驱声称，通常使用的基于重播的课堂学习方法（CIL）是无效的，因此对于FSCIL而言并不是首选。如果真理，这对FSCIL领域产生了重大影响。在本文中，我们通过经验结果表明，采用数据重播非常有利。但是，存储和重播旧数据可能会导致隐私问题。为了解决此问题，我们或建议使用无数据重播，该重播可以通过发电机综合数据而无需访问真实数据。在观察知识蒸馏的不确定数据的有效性时，我们在发电机培训中强加了熵正则化，以鼓励更不确定的例子。此外，我们建议使用单速样标签重新标记生成的数据。这种修改使网络可以通过完全减少交叉渗透损失来学习，从而减轻了在常规知识蒸馏方法中平衡不同目标的问题。最后，我们对CIFAR-100，Miniimagenet和Cub-200展示了广泛的实验结果和分析，以证明我们提出的效果。

随着深度学习模型逐渐成为时间序列预测的主要主力，因此，对抗性攻击下对预测和决策系统的潜在脆弱性已成为近年来的主要问题。尽管对单变量时间序列的预测开始研究这种行为和防御机制，但关于多变量预测的研究仍然很少，由于其在不同时间序列之间编码相关性的能力，通常是优选的。在这项工作中，我们考虑到攻击预算约束和多个时间序列之间的相关结构，研究和设计对多元概率预测模型的对抗性攻击。具体而言，我们研究了稀疏的间接攻击，该攻击仅通过攻击少数其他项目的历史来节省攻击成本，从而损害了项目（时间序列）的预测（时间序列）。为了打击这些攻击，我们还制定了两种防御策略。首先，我们采用随机平滑度到多元时间序列方案，并通过经验实验验证其有效性。其次，我们利用稀疏的攻击者来实现端到端的对抗训练，从而提供强大的概率预测者。对REAL数据集进行的广泛实验证实，与其他基线防御机制相比，我们的攻击方案具有强大的功能，并且我们的防御算法更有效。

从理论上讲，无监督的域适应性（UDA）的成功在很大程度上取决于域间隙估计。但是，对于无源UDA，在适应过程中无法访问源域数据，这在测量域间隙方面构成了巨大挑战。在本文中，我们建议使用许多分类器来学习源域决策边界，即使两个域数据无法同时访问，它也提供了域间隙的更紧密的上限。对源模型进行了训练，可以推开每对分类器，同时确保决策边界的正确性。从这个意义上讲，我们的许多分类器模型尽可能将源不同类别分开，从而诱导目标域中许多分类器的最大分歧，从而最大程度地提高了可转移的源域知识。为了进行适应，源模型适应最大化分类器对之间的一致性。因此，目标特征从决策范围中推开。在UDA的几个数据集上进行的实验表明，我们的方法在免费的UDA方法中实现了最先进的性能，甚至可以竞争为可用的UDA方法竞争。

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.

Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDR) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.