凭借其综合理论和实际相关性，逻辑匪徒最近经历了仔细的审查。这项研究工作提供了统计上有效的算法，通过指数巨大的因素来改善以前的策略的遗憾。然而，这种算法非常昂贵，因为它们需要每轮的$ \ omega（t）$操作。另一方面，一种不同的研究系列专注于计算效率（$ \ mathcal {o}（1）美元的成本），但在放弃上述指数改进的成本上。遗憾的是，获得两个世界的最佳并非结婚两种方法的问题。相反，我们为Logistic Barits介绍了一个新的学习过程。它产生了信心集，可以在没有牺牲统计密封性的情况下轻松在线维护足够的统计数据。结合高效的规划机制，我们设计了快速算法，后悔性能仍然符合Abeille等人的问题依赖性较低。 （2021）。据我们所知，这些是第一个同时享受统计和计算效率的第一逻辑强盗算法。

Maillard（2013）的博士论文呈现了$ k $武装匪徒问题的随机算法。我们呼叫Maillard采样（MS）的这种缺少已知的算法计算以封闭形式选择每个臂的概率，这对于从强盗数据的反事实评估有用，而是缺乏来自汤普森采样，这是一种广泛采用的匪徒行业算法。通过这种优点，我们重新审视MS并进行改进的分析，以表明它实现了渐近最优性和$ \ SQRT {kt \ log {k}} $ minimax后悔绑定在$ t $是时间界限，它与之匹配标准渐近最佳的UCB的性能。然后，我们提出了一个称为MS $ ^ + $的MS的变体，这将改善其最小绑定到$ \ sqrt {kt \ log {k}} $，而不会失去渐近最优值。 $ ^ + $ MS也可以调整为攻击性（即，较少的探索），而不会失去理论担保，从现有强盗算法无法使用的独特功能。我们的数值评估显示了MS $ ^ + $的有效性。

在在线学习问题中，利用低方差在获得紧密性能保证方面发挥着重要作用，但仍然是挑战的，因为差异通常不知道先验。最近，张等人取得了相当大的进展。 （2021）在没有知识的情况下获得用于线性匪徒的方差 - 自适应遗憾，没有知识的差异和对​​线性混合物Markov决策过程（MDP）的无差异的无差异遗憾。在本文中，我们提出了一种新的分析，从而显着改善了他们的遗憾。对于线性匪徒，我们实现$ \ tilde o（d ^ {1.5} \ sqrt {\ sum_ {k} ^ k \ sigma_k ^ 2} + d ^ 2）$ why $ d $是功能的维度$ k $是时间横向，$ \ sigma_k ^ 2 $是时间步骤$ k $的噪声差异，而$ \ tilde o $忽略了polylogarithmic依赖，这是$ d ^ 3 $的因素。对于线性混合MDP，我们达到$ \ tilde o（d ^ {1.5} \ sqrt {k} + d ^ 3）$ white $ d $的地平线遗憾的遗憾遗憾的遗憾 - 是基本型号的数量和$ k $剧集的数量。这是较低的术语和下订单中的持续期限和D ^ 6美元的倍数。我们的分析依稀依赖于新颖的椭圆潜力“计数”的引理。这种引理允许基于剥离的遗憾分析，这可以是独立的兴趣。

统计中的一个经典问题是对样品对随机变量的预期估计。这引起了导出浓度不平等和置信序列的紧密联系的问题，即随着时间的推移均匀保持的置信区间。Jun和Orabona [Colt'19]已经展示了如何轻松将在线投注算法的遗憾保证转化为时均匀的集中度不平等。在本文中，我们表明我们可以进一步发展：我们表明，普遍投资组合算法的遗憾引起了新的隐式时间均匀浓度和最先进的经验计算出的置信序列。特别是，即使使用单个样本，我们的数值获得的置信序列也永远不会空置，并满足迭代对数定律。

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.

This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net based discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel as well as global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.

Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for Metaverse applications and hence, real-time digital twinning of real-world scenes is increasing. Nevertheless, the replication of 2D physical world images into 3D virtual world scenes is computationally intensive and requires computation offloading. The disparity in transmitted scene dimension (2D as opposed to 3D) leads to asymmetric data sizes in uplink (UL) and downlink (DL). To ensure the reliability and low latency of the system, we consider an asynchronous joint UL-DL scenario where in the UL stage, the smaller data size of the physical world scenes captured by multiple extended reality users (XUs) will be uploaded to the Metaverse Console (MC) to be construed and rendered. In the DL stage, the larger-size 3D virtual world scenes need to be transmitted back to the XUs. The decisions pertaining to computation offloading and channel assignment are optimized in the UL stage, and the MC will optimize power allocation for users assigned with a channel in the UL transmission stage. Some problems arise therefrom: (i) interactive multi-process chain, specifically Asynchronous Markov Decision Process (AMDP), (ii) joint optimization in multiple processes, and (iii) high-dimensional objective functions, or hybrid reward scenarios. To ensure the reliability and low latency of the system, we design a novel multi-agent reinforcement learning algorithm structure, namely Asynchronous Actors Hybrid Critic (AAHC). Extensive experiments demonstrate that compared to proposed baselines, AAHC obtains better solutions with preferable training time.