与从头开始的传统学习相比，知识蒸馏有时会使DNN实现卓越的性能。本文提供了一种新的观点，可以根据信息理论来解释知识蒸馏的成功，即量化在DNN的中间层中编码的知识点。为此，我们将DNN中的信号处理视为丢弃层的信息。知识点称为输入单元，其信息比其他输入单元所丢弃的信息要少得多。因此，我们根据知识点的量化提出了三个用于知识蒸馏的假设。 1. DNN从知识蒸馏中学习比从头开始学习的DNN学习更多的知识点。 2.知识蒸馏使DNN更有可能同时学习不同的知识点。相比之下，从头开始的DNN学习倾向于顺序编码各种知识点。 3.与从头开始学习的DNN学习通常更稳定地优化了从知识蒸馏中学习的DNN学习。为了验证上述假设，我们设计了具有前景对象注释的三种类型的指标，以分析DNN的功能表示，\ textit {i.e。}知识点的数量和质量，不同知识点的学习速度，以及优化方向的稳定性。在实验中，我们诊断出各种DNN的不同分类任务，即图像分类，3D点云分类，二进制情感分类和问题回答，这些问题验证了上述假设。

最近的研究表明，通过梯度下降训练的无限宽神经网络（NN）的动态可以是神经切线核（NTK）\ CITEP {Jacot2018neural}的特征。在平方损失下，通过梯度下降训练的无限宽度NN，具有无限小的学习速率等同于与NTK \ CITEP {arora2019Exact}的内核回归。但是，当前ridge回归{arora2019Harnessing}只知道等价物，而NN和其他内核机（KMS）之间的等价，例如，支持向量机（SVM），仍然未知。因此，在这项工作中，我们建议在NN和SVM之间建立等效，具体而言，通过柔软的边缘损失和具有由子润发性培训的NTK培训的标准柔软裕度SVM培训的无限宽NN。我们的主要理论结果包括建立NN和广泛的$ \ ELL_2 $正规化KMS之间的等价，其中有限宽度界限，不能通过事先工作来处理，并显示出通过这种正规化损耗函数训练的每个有限宽度NN大约一公里。此外，我们展示了我们的理论可以实现三种实际应用，包括（i）\ yressit {非空心}通过相应的km界限Nn; （ii）无限宽度NN的\ yryit {非琐碎}鲁棒性证书（而现有的鲁棒性验证方法提供空中界定）; （iii）本质上更强大的无限宽度NN，来自以前的内核回归。我们的实验代码可用于\ URL {https://github.com/leslie-ch/equiv-nn-svm}。

当系统中有某些未知术语和隐藏的物理机制时，基于第一原理的复杂物理系统的管理方程可能会非常具有挑战性。在这项工作中，我们采用深度学习体系结构来学习基于从完全动力学模型中获取的数据的等离子体系统的流体部分微分方程（PDE）。证明了学到的多臂流体PDE可以融合诸如Landau阻尼等动力学效应。基于学习的流体闭合，数据驱动的多音阶流体建模可以很好地再现从完全动力学模型中得出的所有物理量。Landau阻尼的计算阻尼率与完全动力学的模拟和线性理论一致。用于复杂物理系统的PDE的数据驱动的流体建模可以应用于改善流体闭合并降低全球系统多规模建模的计算成本。

计算机辅助诊断（CAD）系统可以为皮肤病的临床诊断提供参考。卷积神经网络（CNN）不仅可以提取视觉元素，例如颜色和形状，而且还可以提取语义特征。因此，他们在皮肤镜检查图像的许多任务中取得了重大改进。皮肤镜检查的成像没有主要方向，表明数据集中有大量的皮肤病变靶旋转。然而，CNN缺乏抗旋转能力，这必然会影响CNN的特征提取能力。我们提出了一个旋转平均值（RM）网络，以从皮肤镜图像中提取旋转不变性特征。在RM中，每组旋转的特征地图对应于一组重量共享卷积输出，并使用MeanOut操作融合以获取最终特征图。通过理论推导，提出的RM网络是旋转等值的，并且在全球平均池（GAP）操作之后，可以提取旋转不变的特征。提取的旋转不变特征可以更好地代表皮肤镜图像的分类和检索任务中的原始数据。提出的RM是一般操作，它不会改变网络结构或增加任何参数，并且可以灵活地嵌入CNN的任何部分。大量实验是在皮肤镜检查图像数据集上进行的。结果表明，我们的方法优于其他抗旋转方法，并在皮肤镜检查图像分类和检索任务方面取得了重大改进，表明在皮肤镜图像领域旋转不变性的潜力。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.