最近在图像染色的作品表明，结构信息在恢复视觉上令人愉悦的结果方面发挥着重要作用。在本文中，我们提出了由基于两个并行发射机的流组成的端到端架构：主流（MS）和结构流（SS）。在SS的帮助下，MS可以产生具有合理结构和现实细节的合理结果。具体地，MS通过同时推断丢失的结构和纹理来重建详细图像，并且SS仅通过从MS的编码器处理分层信息来恢复丢失的结构。通过在培训过程中与SS进行互动，可以暗示MS可以暗示利用结构性提示。为了帮助SS专注于结构并防止MS中的纹理受到影响，提出了一种门控单元来抑制MS和SS之间的信息流中的结构无关激活。此外，SS中的多尺度结构特征映射用于明确指导通过融合块的MS的解码器中的结构合理的图像重建。在Celeba，Paris Streetview和Parume2数据集上进行了广泛的实验表明我们所提出的方法优于最先进的方法。

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption.

Along with the springing up of semantics-empowered communication (SemCom) researches, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present corresponding ecosystems, including theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content \& channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., reliable and goal-oriented communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate the future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.

Natural language prompts have been shown to facilitate cross-task generalization for large language models. However, with no or limited labeled examples, the cross-task performance is highly sensitive to the choice of prompts, while selecting a high-performing prompt is challenging given the scarcity of labels. To address the issue, we propose a Zero-Label Prompt Selection (ZPS) method that selects prompts without any labeled data or gradient update. Specifically, given the candidate human-written prompts for a task, ZPS labels a set of unlabeled data with a prompt ensemble and uses the pseudo-labels for prompt selection. Experiments show that ZPS improves over prior methods by a sizeable margin in zero-label performance. We also extend ZPS to a few-shot setting and show its advantages over strong baselines such as prompt tuning and model tuning.

Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples. Existing metric learning methods compute token-level similarities between query and support sets, but are not able to fully incorporate label semantics into modeling. To address this issue, we propose a simple method to largely improve metric learning for NER: 1) multiple prompt schemas are designed to enhance label semantics; 2) we propose a novel architecture to effectively combine multiple prompt-based representations. Empirically, our method achieves new state-of-the-art (SOTA) results under 16 of the 18 considered settings, substantially outperforming the previous SOTA by an average of 8.84% and a maximum of 34.51% in relative gains of micro F1. Our code is available at https://github.com/AChen-qaq/ProML.

In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following beamforming. But the large scale of antennas in massive MIMO systems causes huge feedback overhead. Deep learning (DL) based methods can compress the CSI at the UE and recover it at the BS, which reduces the feedback cost significantly. But the compressed CSI must be quantized into bit streams for transmission. In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback. First, we design a network-aided adaptor and an advanced training scheme to adaptively improve the quantization and reconstruction accuracy. Moreover, for easy practical employment, we introduce the expert knowledge of data distribution and propose a pluggable and cost-free adaptor scheme. Experiments show that compared with the state-of-the-art feedback quantization method, this adaptor-aided quantization strategy can achieve better quantization accuracy and reconstruction performance with less or no additional cost. The open-source codes are available at https://github.com/zhangxd18/QCRNet.

为了对分布样本进行分类，深层神经网络学习标签 - 歧义表示表示，但是，根据信息瓶颈，这不一定是分布歧视性的。因此，训练有素的网络可以为与分布样本不同的分布样本分配出意外的高信任预测。具体而言，网络从分布样本中提取与标签相关的信息，以学习标签 - 歧义表示表示，但丢弃了与标签相关的弱信息。因此，网络用最小标签敏感的信息作为分布样本将分布式样品视为分布样品。根据分布样本的不同信息性属性，双重表示学习（DRL）方法学习与分配样本的标签无关的分布不同的表示形式，并结合了标签和分布歧义性歧视性。检测到分布样本的表示。对于标签 - 歧义表示形式，DRL通过隐式约束构建了互补分布不同的表示表示，即整合了不同的中间表示，其中中间表示与标签 - 歧义表示表示具有更高的权重。实验表明，DRL的表现优于分布外检测的最新方法。

深度神经网络只会学会将分布输入映射到其在训练阶段的相应地面真实标签，而不会区分分配样本与分布情况。这是由于所有样品都是独立且分布相同而没有分布区别的假设。因此，从分布样品中学到的一个预处理的网络将分布的样本视为分布，并在测试阶段对它们进行高信心预测。为了解决这个问题，我们从培训分配样本附近分布中绘制出分布的样本，以学习拒绝对分数输入的预测。通过假设通过混合多个分发样品而生成的分布样本不会共享其组成部分相同的类别，从而引入了\ textit {跨级附近分布}。因此，我们通过从跨级附近分布中得出的分布样本对列表进行列表来提高预审慎的网络的可区分性，其中每个分布输入输入都对应于互补标签。各种内部/分布数据集的实验表明，所提出的方法在提高区分内部和分发样品的能力方面显着优于现有方法。

当分布（ID）样品与分布外（OOD）样本之间存在差异时，对ID样品进行训练的深神经网络遭受了OOD样品的高信心预测。这主要是由无法使用的OOD样品引起的，以限制培训过程中的网络。为了提高深网的OOD敏感性，几种最先进的方法将其他现实世界数据集的样本作为OOD样本引入训练过程，并将手动确定的标签分配给这些OOD样本。但是，他们牺牲了分类准确性，因为OOD样品的不可靠标记会破坏ID分类。为了平衡ID的概括和OOD检测，要解决的主要挑战是使OOD样本与ID兼容，这在本文中由我们提议的\ textit {监督适应}方法解决，以定义OOD样本的适应性监督信息。首先，通过通过共同信息来测量ID样本及其标签之间的依赖关系，我们根据所有类别的负概率揭示了监督信息的形式。其次，在通过解决多个二进制回归问题来探索ID和OOD样本之间的数据相关性之后，我们估算了监督信息以使ID类更可分离。我们使用两个ID数据集和11个OOD数据集对四个高级网络体系结构进行实验，以证明我们的监督适应方法在实现ID分类能力和OOD检测能力方面的平衡效果。

无法保证专家注释的培训数据的质量，甚至对于由分发样本组成的非IID数据（即，分布式和分布式样本都具有不同的分布），更是如此。 。专家可能会错误地注释与分布样本相同的分发样品，从而产生不可信的地面真相标签。学习这种非IID数据混合与不信任标签的分布样品混合在一起，既浅层和深度学习都有显着挑战，没有报告相关工作。可以识别样本的值得信赖的互补标签，指示其不属于哪些类，因为除分布外样品和分布外样品都不属于类别外，除了与地面真实标签相对应的类别。有了这个见解，我们提出了一种新颖的\ textit {灰色学习}方法，可以从非IID数据中学习具有分布式和分离外样品的非IID数据。由于训练样本的不确定分布，我们拒绝了低信心输入的互补标签，同时将高信心输入映射到培训中的地面真相标签。在统计学习理论的基础上，我们得出了概括误差，该误差表明灰色学习在非IID数据上实现了紧密的束缚。广泛的实验表明，我们的方法对可靠统计的替代方法提供了重大改进。