经过标准的横向损失训练的深度神经网络更容易记住嘈杂的标签，从而降低了其性能。当嘈杂的标签干预时，使用互补标签的负面学习更加健壮，但模型收敛速度极慢。在本文中，我们首先引入了双向学习方案，在这种方案中，积极的学习可确保收敛速度，而负面学习则可以与标签噪声保持稳健的应对。此外，提出了一种动态样本重新加权策略，以通过利用负面学习对样本概率分布的出色歧视能力来削弱噪声标记样品的影响。此外，我们结合了自我鉴定，以进一步提高模型性能。该代码可在\ url {https://github.com/chenchenzong/bldr}中获得。

部分标签学习（PLL）是一个典型的弱监督学习框架，每个培训实例都与候选标签集相关联，其中只有一个标签是有效的。为了解决PLL问题，通常方法试图通过使用先验知识（例如培训数据的结构信息）或以自训练方式提炼模型输出来对候选人集进行歧义。不幸的是，由于在模型训练的早期阶段缺乏先前的信息或不可靠的预测，这些方法通常无法获得有利的性能。在本文中，我们提出了一个新的针对部分标签学习的框架，该框架具有元客观指导性的歧义（MOGD），该框架旨在通过在小验证集中求解元目标来从设置的候选标签中恢复地面真相标签。具体而言，为了减轻假阳性标签的负面影响，我们根据验证集的元损失重新权重。然后，分类器通过最大程度地减少加权交叉熵损失来训练。通过使用普通SGD优化器的各种深网络可以轻松实现所提出的方法。从理论上讲，我们证明了元目标的收敛属性，并得出了所提出方法的估计误差界限。在各种基准数据集和实际PLL数据集上进行的广泛实验表明，与最先进的方法相比，所提出的方法可以实现合理的性能。

在本文中，我们研究了部分多标签（PML）图像分类问题，其中每个图像都用候选标签集注释，由多个相关标签和其他嘈杂标签组成。现有的PML方法通常会设计一种歧义策略来通过利用具有额外假设的先验知识来滤除嘈杂的标签，但不幸的是，这在许多实际任务中都无法使用。此外，由于歧义的目标函数通常是在整个训练集中精心设计的，因此在小型批次上使用SGD的深层模型中几乎无法优化它。在本文中，我们第一次提出了一个深层模型，以增强表示能力和歧视能力。一方面，我们提出了一种新型的基于课程的放弃策略，以通过融合不同类别的各种困难来逐步识别地面真相标签。另一方面，引入了一个一致性正规化，以供模型重新培训，以平衡拟合的易于标签并利用潜在的相关标签。对常用基准数据集的广泛实验结果表明，所提出的方法显着优于SOTA方法。

删除攻击旨在通过略微扰动正确标记的训练示例的特征来大幅恶化学习模型的测试准确性。通过将这种恶意攻击正式地找到特定$ \ infty $ -wassersein球中的最坏情况培训数据，我们表明最小化扰动数据的对抗性风险相当于优化原始数据上的自然风险的上限。这意味着对抗性培训可以作为防止妄想攻击的原则防御。因此，通过普遍训练可以很大程度地回收测试精度。为了进一步了解国防的内部机制，我们披露了对抗性培训可以通过防止学习者过于依赖于自然环境中的非鲁棒特征来抵制妄想扰动。最后，我们将我们的理论调查结果与一系列关于流行的基准数据集进行了补充，这表明防御能够承受六种不同的实际攻击。在面对令人难以闻名的对手时，理论和经验结果投票给逆势训练。

Recent aerial object detection models rely on a large amount of labeled training data, which requires unaffordable manual labeling costs in large aerial scenes with dense objects. Active learning is effective in reducing the data labeling cost by selectively querying the informative and representative unlabelled samples. However, existing active learning methods are mainly with class-balanced setting and image-based querying for generic object detection tasks, which are less applicable to aerial object detection scenario due to the long-tailed class distribution and dense small objects in aerial scenes. In this paper, we propose a novel active learning method for cost-effective aerial object detection. Specifically, both object-level and image-level informativeness are considered in the object selection to refrain from redundant and myopic querying. Besides, an easy-to-use class-balancing criterion is incorporated to favor the minority objects to alleviate the long-tailed class distribution problem in model training. To fully utilize the queried information, we further devise a training loss to mine the latent knowledge in the undiscovered image regions. Extensive experiments are conducted on the DOTA-v1.0 and DOTA-v2.0 benchmarks to validate the effectiveness of the proposed method. The results show that it can save more than 75% of the labeling cost to reach the same performance compared to the baselines and state-of-the-art active object detection methods. Code is available at https://github.com/ZJW700/MUS-CDB

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.