In recent years, the field of intelligent transportation systems (ITS) has achieved remarkable success, which is mainly due to the large amount of available annotation data. However, obtaining these annotated data has to afford expensive costs in reality. Therefore, a more realistic strategy is to leverage semi-supervised learning (SSL) with a small amount of labeled data and a large amount of unlabeled data. Typically, semantic consistency regularization and the two-stage learning methods of decoupling feature extraction and classification have been proven effective. Nevertheless, representation learning only limited to semantic consistency regularization may not guarantee the separation or discriminability of representations of samples with different semantics; due to the inherent limitations of the two-stage learning methods, the extracted features may not match the specific downstream tasks. In order to deal with the above drawbacks, this paper proposes an end-to-end deep semi-supervised learning double contrast of semantic and feature, which extracts effective tasks specific discriminative features by contrasting the semantics/features of positive and negative augmented samples pairs. Moreover, we leverage information theory to explain the rationality of double contrast of semantics and features and slack mutual information to contrastive loss in a simpler way. Finally, the effectiveness of our method is verified in benchmark datasets.

最近，对抗性训练已被纳入自我监督的对比预训练中，以增强标签效率，并具有令人兴奋的对抗性鲁棒性。但是，鲁棒性是经过昂贵的对抗训练的代价。在本文中，我们表明了一个令人惊讶的事实，即对比的预训练与稳健性具有有趣而隐含的联系，并且在经过训练的代表中如此自然的鲁棒性使我们能够设计出一种强大的鲁棒算法，以防止对抗性攻击，Rush，将标准组合在一起。对比的预训练和随机平滑。它提高了标准准确性和强大的精度，并且与对抗训练相比，培训成本大大降低了。我们使用广泛的经验研究表明，拟议中的Rush在一阶攻击下的共同基准（CIFAR-10，CIFAR-100和STL-10）的大幅度优于对抗性训练的强大分类器。特别是，在$ \ ell _ {\ infty} $下 - 大小为8/255 PGD攻击CIFAR-10的标准扰动，我们使用RESNET-18作为骨架达到77.8％的型号达到77.8％稳健精度和87.9％的标准精度。与最先进的工作相比，我们的工作的鲁棒精度提高了15％以上，标准准确性略有提高。

近年来，人们对深度学习的公平性提出了越来越多的关注。现有的公平感知机器学习方法主要集中于分布数据的公平性。但是，在实际应用程序中，通常在培训数据和测试数据之间进行分配转移是很常见的。在本文中，我们首先表明，现有方法实现的公平性可以通过轻微的分配变化很容易打破。为了解决这个问题，我们提出了一种称为曲率匹配（CUMA）的新型公平学习方法，该方法可以实现可概括的公平性，可概括为具有不明分布变化的看不见的域。具体而言，CUMA通过与两组的损耗曲率分布相匹配，从而实施该模型对多数和少数群体具有相似的概括能力。我们在三个流行的公平数据集上评估我们的方法。与现有方法相比，CUMA在看不见的分布变化下实现了卓越的公平性，而无需牺牲整体准确性或分布公平。

我们考虑临床应用异常定位问题。虽然深入学习推动了最近的医学成像进展，但许多临床挑战都没有完全解决，限制了其更广泛的使用。虽然最近的方法报告了高的诊断准确性，但医生因普遍缺乏算法决策和解释性而涉及诊断决策的这些算法，这是关注这些算法。解决这个问题的一种潜在方法是进一步培训这些模型，以便除了分类它们之外，除了分类。然而，准确地进行这一临床专家需要大量的疾病定位注释，这是对大多数应用程序来实现昂贵的任务。在这项工作中，我们通过一种新的注意力弱监督算法来解决这些问题，该弱势监督算法包括分层关注挖掘框架，可以以整体方式统一激活和基于梯度的视觉关注。我们的关键算法创新包括明确序号注意约束的设计，实现了以弱监督的方式实现了原则的模型培训，同时还通过本地化线索促进了产生视觉关注驱动的模型解释。在两个大型胸部X射线数据集（NIH Chescx-Ray14和Chexpert）上，我们展示了对现有技术的显着本地化性能，同时也实现了竞争的分类性能。我们的代码可在https://github.com/oyxhust/ham上找到。

联合学习（FL）是一种流行的分布式学习模式，它可以从一组参与用户中学习模型而无需共享原始数据。 FL的一个主要挑战是异质用户，他们的分布不同（或非IID）数据和不同的计算资源。由于联合用户将使用该模型进行预测，因此他们经常要求训练有素的模型在测试时对恶意攻击者保持强大的态度。尽管对抗性培训（AT）为集中学习提供了一个合理的解决方案，但扩大对联合用户的使用范围已经引起了重大挑战，因为许多用户可能拥有非常有限的培训数据和严格的计算预算，以负担得起数据繁殖和成本高昂。在本文中，我们研究了一种新颖的FL策略：在联邦学习期间，从可负担得起的富裕用户的富裕用户传播对抗性的鲁棒性。我们表明，现有的FL技术不能与非IID用户之间稳健性的策略有效整合，并通过正确使用批处理规范化提出了有效的传播方法。我们通过广泛的实验证明了我们方法的合理性和有效性。特别是，即使只有一小部分用户在学习过程中，提出的方法也证明可以赋予联合模型出色的鲁棒性。源代码将发布。

本文回顾了关于压缩视频质量增强质量的第一个NTIRE挑战，重点是拟议的方法和结果。在此挑战中，采用了新的大型不同视频（LDV）数据集。挑战有三个曲目。Track 1和2的目标是增强HEVC在固定QP上压缩的视频，而Track 3旨在增强X265压缩的视频，以固定的位速率压缩。此外，轨道1和3的质量提高了提高保真度（PSNR）的目标，以及提高感知质量的2个目标。这三个曲目完全吸引了482个注册。在测试阶段，分别提交了12个团队，8支球队和11支球队，分别提交了轨道1、2和3的最终结果。拟议的方法和解决方案衡量视频质量增强的最先进。挑战的首页：https：//github.com/renyang-home/ntire21_venh

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices' computing resources compromise the practical effectiveness of PCA. We propose a federated PCA-based Grassmannian optimization framework that coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices' traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and the absence of detecting latency using limited computational resources. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches. Finally, we show that the Grassmann manifold algorithm is highly adapted for IoT anomaly detection, which permits drastically reducing the analysis time of the system. To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.

Learning rich skills through temporal abstractions without supervision of external rewards is at the frontier of Reinforcement Learning research. Existing works mainly fall into two distinctive categories: variational and Laplacian-based option discovery. The former maximizes the diversity of the discovered options through a mutual information loss but overlooks coverage of the state space, while the latter focuses on improving the coverage of options by increasing connectivity during exploration, but does not consider diversity. In this paper, we propose a unified framework that quantifies diversity and coverage through a novel use of the Determinantal Point Process (DPP) and enables unsupervised option discovery explicitly optimizing both objectives. Specifically, we define the DPP kernel matrix with the Laplacian spectrum of the state transition graph and use the expected mode number in the trajectories as the objective to capture and enhance both diversity and coverage of the learned options. The proposed option discovery algorithm is extensively evaluated using challenging tasks built with Mujoco and Atari, demonstrating that our proposed algorithm substantially outperforms SOTA baselines from both diversity- and coverage-driven categories. The codes are available at https://github.com/LucasCJYSDL/ODPP.

Opinion summarisation synthesises opinions expressed in a group of documents discussing the same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such posts are noisy and have unpredictable structure, posing additional challenges for the construction of the summary distribution and the preservation of meaning compared to online reviews, which has been so far the focus of opinion summarisation. To address these challenges we present \textit{WassOS}, an unsupervised abstractive summarization model which makes use of the Wasserstein distance. A Variational Autoencoder is used to get the distribution of documents/posts, and the distributions are disentangled into separate semantic and syntactic spaces. The summary distribution is obtained using the Wasserstein barycenter of the semantic and syntactic distributions. A latent variable sampled from the summary distribution is fed into a GRU decoder with a transformer layer to produce the final summary. Our experiments on multiple datasets including Twitter clusters, Reddit threads, and reviews show that WassOS almost always outperforms the state-of-the-art on ROUGE metrics and consistently produces the best summaries with respect to meaning preservation according to human evaluations.

Comparing Bayesian neural networks (BNNs) with different widths is challenging because, as the width increases, multiple model properties change simultaneously, and, inference in the finite-width case is intractable. In this work, we empirically compare finite- and infinite-width BNNs, and provide quantitative and qualitative explanations for their performance difference. We find that when the model is mis-specified, increasing width can hurt BNN performance. In these cases, we provide evidence that finite-width BNNs generalize better partially due to the properties of their frequency spectrum that allows them to adapt under model mismatch.