由于其快速响应时间和高度的鲁棒性，选择性固定过滤器主动噪声控制（SFANC）方法似乎是在各种实用的活动噪声控制（ANC）系统中广泛使用的可行候选者。与常规的固定过滤器ANC方法相比，SFANC可以为不同类型的噪声选择预训练的控制过滤器。因此，深度学习技术可以用于SFANC方法中，以使最适当的控制过滤器更灵活地选择衰减各种噪声。此外，在深层神经网络的帮助下，可以自动从噪声数据而不是通过试用和错误来学习选择策略，从而大大简化和改善了ANC设计的可实用性。因此，本文研究了基于不同一维和二维卷积神经网络的SFANC的性能。此外，我们对几种网络培训策略进行了比较分析，并发现微调可以改善选择性的性能。

选择性固定过滤器活动噪声控制（SFANC）方法为各种类型的噪声选择最佳的预训练的控制过滤器可以达到快速响应时间。但是，由于滤波器的选择不准确和缺乏适应性，可能导致稳态错误。相比之下，过滤的X归一化最小均值（FXNLMS）算法可以通过自适应优化获得较低的稳态误差。尽管如此，其缓慢的收敛对动态噪声衰减产生了不利影响。因此，本文提出了一种混合SFANC-FXNLMS方法来克服自适应算法的缓慢收敛性，并提供比SFANC方法更好的降噪水平。轻量级的一维卷积神经网络（1D CNN）旨在自动为主噪声的每个框架选择最合适的预训练的控制滤波器。同时，FXNLMS算法继续以采样率更新所选预训练的对照滤波器的系数。由于两种算法的有效组合，实验结果表明，混合SFANC-FXNLMS算法可以达到快速响应时间，低噪声误差和高度的鲁棒性。

音景研究的生态有效性通常取决于代表正在研究的知觉空间的声景选择。例如，声景愉快的研究可能会调查来自“宜人”到“烦人”的音景地点。音景的选择通常是研究人员主导的，但是参与者主导的过程可以降低选择偏见并提高结果可靠性。因此，我们提出了一种强大的参与者指导的方法，以查明具有任意感知属性的特征音景。我们通过识别跨越从ISO 12913-2的Soundscape感知的ISO 12913-2 Circumplex模型的新加坡音景来验证我们的方法。从记忆和经验来看，有67名参与者首先选择了与新加坡每个主要计划区域中每个感知象限相对应的位置。然后，我们在选定的位置进行了加权K-均值聚类，每个位置的权重从每个参与者在每个位置花费的频率和持续时间得出。因此，权重是参与者信心的代理。因此，总共将62个位置确定为具有特征性音景的合适位置，可利用ISO 12913-2感知象限进行进一步研究。声音景观的视听记录和声学表征将在以后的研究中进行。

在音景增强系统中的掩蔽器和播放增益水平的选择对于改善给定环境的整体声学舒适度至关重要。传统上，选择适当的掩蔽者和增益水平的专家意见为可能无法代表目标人群或通过聆听测试而告知，这可能是耗时且富有劳动力的。此外，掩蔽器和增益的产生静态选择通常对现实世界中的动态性质不灵活。在这项工作中，我们利用了深度学习模型来执行最佳掩蔽器的联合选择及其对给定音景的增益水平。所提出的模型是使用高度模块化的构建块设计的，可以进行优化的推理过程，该过程可以快速搜索大量掩膜和增益组合。此外，我们介绍了以数字增益水平为条件的特征域音景增强，从而消除了推理期间的计算昂贵的波形 - 域混合过程，以及新的掩护者所需的乏味的预校准过程。在大规模的数据集上对拟议的系统进行了验证，该数据集对具有440多名参与者的增强音景的主观响应，以确保模型预测掩护者的联合效果及其在感知愉悦水平上的增益水平的能力。

This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesirable distortions on identity features, which is not always favorable in id-sensitive ReID tasks. In this paper, we propose to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning. A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. Deviating from previous GAN-based ReID methods that only work in id-unrelated space (pose and camera style), we conduct GAN-based augmentation on both id-unrelated and id-related features. We further propose specific contrastive losses to help our network learn invariance from id-unrelated and id-related augmentations. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.

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.

With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making. Thus, it is critical to explain why graph neural network (GNN) makes particular predictions for them to be believed in many applications. Some GNNs explainers have been proposed recently. However, they lack to generate accurate and real explanations. To mitigate these limitations, we propose GANExplainer, based on Generative Adversarial Network (GAN) architecture. GANExplainer is composed of a generator to create explanations and a discriminator to assist with the Generator development. We investigate the explanation accuracy of our models by comparing the performance of GANExplainer with other state-of-the-art methods. Our empirical results on synthetic datasets indicate that GANExplainer improves explanation accuracy by up to 35\% compared to its alternatives.

This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. We train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. With the modified CGAN framework, various forms of semantic insights gathered from the query document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. Our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models

High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. However, due to the combinatorial explosion of the search space when the HUSPM problem encounters a low utility threshold or large-scale data, it may be time-consuming and memory-costly to address the HUSPM problem. Several algorithms have been proposed for addressing this problem, but they still cost a lot in terms of running time and memory usage. In this paper, to further solve this problem efficiently, we design a compact structure called sequence projection (seqPro) and propose an efficient algorithm, namely discovering high-utility sequential patterns with the seqPro structure (HUSP-SP). HUSP-SP utilizes the compact seq-array to store the necessary information in a sequence database. The seqPro structure is designed to efficiently calculate candidate patterns' utilities and upper bound values. Furthermore, a new upper bound on utility, namely tighter reduced sequence utility (TRSU) and two pruning strategies in search space, are utilized to improve the mining performance of HUSP-SP. Experimental results on both synthetic and real-life datasets show that HUSP-SP can significantly outperform the state-of-the-art algorithms in terms of running time, memory usage, search space pruning efficiency, and scalability.

Despite excellent performance in image generation, Generative Adversarial Networks (GANs) are notorious for its requirements of enormous storage and intensive computation. As an awesome ''performance maker'', knowledge distillation is demonstrated to be particularly efficacious in exploring low-priced GANs. In this paper, we investigate the irreplaceability of teacher discriminator and present an inventive discriminator-cooperated distillation, abbreviated as DCD, towards refining better feature maps from the generator. In contrast to conventional pixel-to-pixel match methods in feature map distillation, our DCD utilizes teacher discriminator as a transformation to drive intermediate results of the student generator to be perceptually close to corresponding outputs of the teacher generator. Furthermore, in order to mitigate mode collapse in GAN compression, we construct a collaborative adversarial training paradigm where the teacher discriminator is from scratch established to co-train with student generator in company with our DCD. Our DCD shows superior results compared with existing GAN compression methods. For instance, after reducing over 40x MACs and 80x parameters of CycleGAN, we well decrease FID metric from 61.53 to 48.24 while the current SoTA method merely has 51.92. This work's source code has been made accessible at https://github.com/poopit/DCD-official.