Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc. In particular, the endless patterns of background changes require detectors to have a high generalization against unseen environment variations, making this task significantly challenging. Recent deep learning-based methods develop novel network architectures or optimization strategies with paired-training examples, which do not handle the generalization issue explicitly and require huge manual pixel-level annotation efforts. In this work, for the first attempt in the CD community, we study the generalization issue of CD from the perspective of data augmentation and develop a novel weakly supervised training algorithm that only needs image-level labels. Different from general augmentation techniques for classification, we propose the background-mixed augmentation that is specifically designed for change detection by augmenting examples under the guidance of a set of background-changing images and letting deep CD models see diverse environment variations. Moreover, we propose the augmented & real data consistency loss that encourages the generalization increase significantly. Our method as a general framework can enhance a wide range of existing deep learning-based detectors. We conduct extensive experiments in two public datasets and enhance four state-of-the-art methods, demonstrating the advantages of our method. We release the code at https://github.com/tsingqguo/bgmix.

在高光谱图像分类（HSI）任务中，忽略了包括有关土地覆盖类别的大量先验知识在内的文本信息。有必要探索语言模式在协助HSI分类方面的有效性。此外，大规模训练的图像文本基础模型在各种下游应用中都表现出了出色的性能，包括零拍传输。但是，大多数领域的概括方法从未解决过采矿语言模态知识以提高模型的概括性能。为了弥补上述不足的不足，提出了一个语言感知的域概括网络（LDGNET），以从跨域共享的先验知识中学习跨域不变的表示。所提出的方法仅在源域（SD）上训练，然后将模型传输到目标域（TD）。包括图像编码器和文本编码器在内的双流架构用于提取视觉和语言特征，其中粗粒和细粒度的文本表示旨在提取两个层次的语言特征。此外，语言特征被用作跨域共享的语义空间，并且通过在语义空间中的对比度学习完成视觉语言对齐。与最先进的技术相比，三个数据集上的广泛实验证明了该方法的优越性。

目前，跨景元的高光谱图像（HSI）分类引起了人们的注意。当需要实时处理TD且不能重复使用训练时，必须仅在源域（SD）上训练模型（SD）并将模型直接传输到目标域（TD）。基于域概括的思想，开发了单源域扩展网络（SDENET），以确保域扩展的可靠性和有效性。该方法使用生成的对抗学习在SD中训练和TD测试。包括语义编码器和MORPH编码器在内的发电机旨在基于编码器随机化架构生成扩展域（ED），其中空间和频谱随机化专门用于生成可变的空间和光谱信息，并隐含形态知识。作为域扩展过程中的域不变信息。此外，受监督的对比学习被采用在歧视者中，以学习阶级领域不变的表示，该表示驱动了SD和ED的阶级样本。同时，对抗性训练旨在优化发电机以驱动SD和ED的阶级样品进行分离。与最先进的技术相比，在两个公共HSI数据集和另一个多光谱图像（MSI）数据集上进行了广泛的实验，证明了该方法的优越性。

电子设计自动化（EDA）社区一直在积极探索非常大规模的计算机辅助设计（VLSI CAD）的机器学习。许多研究探索了基于学习的技术，用于设计流中的跨阶段预测任务，以实现更快的设计收敛。尽管建筑机器学习（ML）模型通常需要大量数据，但由于缺乏大型公共数据集，大多数研究只能生成小型内部数据集进行验证。在本文中，我们介绍了第一个用于机器学习任务的开源数据集，称为CircuitNet。该数据集由基于6种开源RISC-V设计的商业设计工具的多功能运行中提取的10K以上样品组成。

由于简单但有效的训练机制和出色的图像产生质量，生成的对抗网络（GAN）引起了极大的关注。具有生成照片现实的高分辨率（例如$ 1024 \ times1024 $）的能力，最近的GAN模型已大大缩小了生成的图像与真实图像之间的差距。因此，许多最近的作品表明，通过利用良好的潜在空间和博学的gan先验来利用预先训练的GAN模型的新兴兴趣。在本文中，我们简要回顾了从三个方面利用预先培训的大规模GAN模型的最新进展，即1）大规模生成对抗网络的培训，2）探索和理解预训练的GAN模型，以及预先培训的GAN模型，以及3）利用这些模型进行后续任务，例如图像恢复和编辑。有关相关方法和存储库的更多信息，请访问https://github.com/csmliu/pretretaining-gans。

一击生成域Adaption旨在仅使用一个参考图像将一个预训练的发电机传输到一个新域中。但是，适用的生成器（i）要生成从预训练的生成器继承的多种图像，而（ii）（ii）忠实地获取参考图像的特定领域特定属性和样式，这仍然非常具有挑战性。在本文中，我们提出了一种新颖的单发性生成域适应方法，即Difa，用于多元化和忠实的适应。对于全球级别的适应，我们利用参考图像的剪辑嵌入与源图像的平均嵌入之间的差异来限制目标发生器。对于本地级别的适应，我们引入了一个细心的样式损失，该损失将每个适应图像的中间令牌与参考图像的相应令牌保持一致。为了促进多样化的生成，引入了选择性的跨域一致性，以选择和保留域共享属性，以编辑潜在的$ \ MATHCAL {W}+$ $空间来继承预训练的生成器的多样性。广泛的实验表明，我们的方法在定量和定性上都优于最先进的实验，尤其是对于大域间隙的情况。此外，我们的DIFA可以轻松地扩展到零击生成域的适应性，并具有吸引力的结果。代码可从https://github.com/1170300521/difa获得。

预先接受的语言模型实现了最先进的导致各种自然语言处理（NLP）任务。 GPT-3表明，缩放预先训练的语言模型可以进一步利用它们的巨大潜力。最近提出了一个名为Ernie 3.0的统一框架，以预先培训大型知识增强型号，并培训了具有10亿参数的模型。 Ernie 3.0在各种NLP任务上表现出最先进的模型。为了探讨缩放的表现，我们培养了百卢比的3.0泰坦参数型号，在PaddlePaddle平台上有高达260亿参数的泰坦。此外，我们设计了一种自我监督的对抗性损失和可控语言建模损失，以使ERNIE 3.0 TITAN产生可信和可控的文本。为了减少计算开销和碳排放，我们向Ernie 3.0泰坦提出了一个在线蒸馏框架，教师模型将同时教授学生和培训。埃塞尼3.0泰坦是迄今为止最大的中国密集预训练模型。经验结果表明，Ernie 3.0泰坦在68个NLP数据集中优于最先进的模型。

乐器识别是广泛应用的音乐信息检索应用。由于以前的大多数乐器识别数据集专注于西方乐器，研究人员很难研究和评估传统的中国乐器识别领域。本文提出了传统的中国音乐数据集，用于培训模型和绩效评估，名为Chmusic。此数据集是免费且公开的，11名中国传统音乐仪器和55名繁体中文音乐摘录在此数据集中录制。然后基于Chmusic数据集提出了评估标准。通过本标准，研究人员可以按照相同规则进行比较它们的结果，不同的研究人员的结果将变得可比。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.