ProtoPNet and its follow-up variants (ProtoPNets) have attracted broad research interest for their intrinsic interpretability from prototypes and comparable accuracy to non-interpretable counterparts. However, it has been recently found that the interpretability of prototypes can be corrupted due to the semantic gap between similarity in latent space and that in input space. In this work, we make the first attempt to quantitatively evaluate the interpretability of prototype-based explanations, rather than solely qualitative evaluations by some visualization examples, which can be easily misled by cherry picks. To this end, we propose two evaluation metrics, termed consistency score and stability score, to evaluate the explanation consistency cross images and the explanation robustness against perturbations, both of which are essential for explanations taken into practice. Furthermore, we propose a shallow-deep feature alignment (SDFA) module and a score aggregation (SA) module to improve the interpretability of prototypes. We conduct systematical evaluation experiments and substantial discussions to uncover the interpretability of existing ProtoPNets. Experiments demonstrate that our method achieves significantly superior performance to the state-of-the-arts, under both the conventional qualitative evaluations and the proposed quantitative evaluations, in both accuracy and interpretability. Codes are available at https://github.com/hqhQAQ/EvalProtoPNet.

Semantic segmentation based on sparse annotation has advanced in recent years. It labels only part of each object in the image, leaving the remainder unlabeled. Most of the existing approaches are time-consuming and often necessitate a multi-stage training strategy. In this work, we propose a simple yet effective sparse annotated semantic segmentation framework based on segformer, dubbed SASFormer, that achieves remarkable performance. Specifically, the framework first generates hierarchical patch attention maps, which are then multiplied by the network predictions to produce correlated regions separated by valid labels. Besides, we also introduce the affinity loss to ensure consistency between the features of correlation results and network predictions. Extensive experiments showcase that our proposed approach is superior to existing methods and achieves cutting-edge performance. The source code is available at \url{https://github.com/su-hui-zz/SASFormer}.

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation.

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of deep learning, Deep RL (DRL) has witnessed great success over a wide spectrum of complex control tasks. Despite the encouraging results achieved, the deep neural network-based backbone is widely deemed as a black box that impedes practitioners to trust and employ trained agents in realistic scenarios where high security and reliability are essential. To alleviate this issue, a large volume of literature devoted to shedding light on the inner workings of the intelligent agents has been proposed, by constructing intrinsic interpretability or post-hoc explainability. In this survey, we provide a comprehensive review of existing works on eXplainable RL (XRL) and introduce a new taxonomy where prior works are clearly categorized into model-explaining, reward-explaining, state-explaining, and task-explaining methods. We also review and highlight RL methods that conversely leverage human knowledge to promote learning efficiency and performance of agents while this kind of method is often ignored in XRL field. Some challenges and opportunities in XRL are discussed. This survey intends to provide a high-level summarization of XRL and to motivate future research on more effective XRL solutions. Corresponding open source codes are collected and categorized at https://github.com/Plankson/awesome-explainable-reinforcement-learning.

神经网络（NNS）和决策树（DTS）都是机器学习的流行模型，但具有相互排斥的优势和局限性。为了带来两个世界中的最好，提出了各种方法来明确或隐式地集成NN和DTS。在这项调查中，这些方法是在我们称为神经树（NTS）的学校中组织的。这项调查旨在对NTS进行全面审查，并尝试确定它们如何增强模型的解释性。我们首先提出了NTS的彻底分类学，该分类法表达了NNS和DTS的逐步整合和共同进化。之后，我们根据NTS的解释性和绩效分析，并建议解决其余挑战的可能解决方案。最后，这项调查以讨论有条件计算和向该领域的有希望的方向进行讨论结束。该调查中审查的论文列表及其相应的代码可在以下网址获得：https：//github.com/zju-vipa/awesome-neural-trees

原型零件网络（Protopnet）引起了广泛的关注，并增加了许多随访研究，因为它的自我解释特性可解释人工智能（XAI）。但是，当直接在视觉变压器（VIT）骨架上应用原始网络时，学到的原型存在“分心”问题：它们具有相对较高的可能性，即被背景激活，并且对前景的关注较少。建模长期依赖性的强大能力使得基于变压器的Protopnet难以专注于原型部分，从而严重损害了其固有的解释性。本文提出了原型零件变压器（ProtoPformer），以适当有效地应用基于原型的方法，并使用VIT进行可解释的图像识别。提出的方法介绍了根据VIT的建筑特征捕获和突出目标的代表性整体和部分特征的全局和局部原型。采用了全球原型，以提供对象的全球视图，以指导本地原型集中在前景上，同时消除背景的影响。之后，明确监督局部原型，以专注于它们各自的原型视觉部分，从而提高整体可解释性。广泛的实验表明，我们提出的全球和本地原型可以相互纠正并共同做出最终决策，这些决策分别忠实，透明地从整体和地方的角度缔合过程。此外，ProtoPformer始终取得优于基于原型的原型基线（SOTA）的卓越性能和可视化结果。我们的代码已在https://github.com/zju-vipa/protopformer上发布。

弱监督的对象本地化是一项具有挑战性的任务，旨在将对象定位具有粗糙注释（例如图像类别）。现有的深网方法主要基于类激活图，该图的重点是突出显示歧视性局部区域，同时忽略了整个对象。此外，基于变压器的技术不断地重点放在阻碍识别完整对象的能力的背景上。为了解决这些问题，我们提出了一种称为令牌改进变压器（TRT）的重新注意事项机制，该机制捕获了对象级语义，以很好地指导本地化。具体而言，TRT引入了一个名为令牌优先级评分模块（TPSM）的新型模块，以抑制背景噪声的效果，同时重点放在目标对象上。然后，我们将类激活图作为语义意识的输入合并，以将注意力图限制为目标对象。在两个基准测试上进行的广泛实验展示了我们提出的方法与现有方法的优势，该方法具有带有图像类别注释的现有方法。源代码可在\ url {https://github.com/su-hui-zz/reattentiontransformer}中获得。

在本文中，我们探讨了一个新的知识障碍问题，称为联合选择性聚合（FEDSA）。 FEDSA的目的是在几位分散的教师的帮助下培训学生模型，以完成一项新任务，他们的预培训任务和数据是不同且不可知的。我们调查此类问题设置的动机源于最近的模型共享困境。许多研究人员或机构已经在培训大型且称职的网络上花费了巨大的资源。由于隐私，安全或知识产权问题，他们也无法分享自己的预培训模型，即使他们希望为社区做出贡献。拟议的FEDSA提供了解决这一困境的解决方案，并使其更进一步，因为学识渊博的学生可以专门从事与所有老师不同的新任务。为此，我们提出了一种处理FEDSA的专门战略。具体而言，我们的学生培训过程是由一种新型的基于显着性的方法驱动的，该方法可以适应教师作为参与者，并将其代表性能力融入到学生中。为了评估FEDSA的有效性，我们在单任务和多任务设置上进行实验。实验结果表明，FEDSA有效地将分散模型的知识融合在一起，并将竞争性能达到集中式基准。

异常检测旨在识别正常数据分布的偏差样本。对比学习提供了一种成功的样本表示方式，可以有效地歧视异常。但是，当在半监督环境下设置的训练中被未标记的异常样本污染时，当前基于对比的方法通常1）忽略训练数据之间的全面关系，导致次优的性能，2）需要微调，导致低效率的低效率。为了解决上述两个问题，在本文中，我们提出了一种新型的分层半监督对比学习（HSCL）框架，以抗污染异常检测。具体而言，HSCL分层调节了三个互补关系：样本到样本，样本到原型型和正常关系，通过对受污染数据的全面探索，扩大了正常样本和异常样本之间的歧视。此外，HSCL是一种端到端的学习方法，可以在不进行微调的情况下有效地学习判别性表示。 HSCL在多种方案中实现了最先进的性能，例如单级分类和跨数据库检测。广泛的消融研究进一步验证了每个考虑的关系的有效性。该代码可在https://github.com/gaoangw/hscl上找到。

最先进的参数和非参数样式转移方法容易导致由于全局统计的对准而导致的本地样式模式，或者由于补丁不匹配而导致的不愉快的人工制品。在本文中，我们研究了一种新型的半参数神经风格转移框架，可减轻参数和非参数风格的缺乏。我们方法的核心思想是使用图神经网络（GNN）建立准确且细粒的内容样式对应关系。为此，我们开发了一个详细的GNN模型，其中包含内容和样式的本地补丁作为图形顶点。然后，将样式转移过程建模为基于注意力的异质消息，以可学习的方式在样式和内容节点之间传递，从而导致本地补丁级别的自适应多一对一风格的相关性。此外，引入了详细的可变形图卷积操作，以进行跨尺度样式符合匹配。实验结果表明，所提出的半参数图像样式化方法可为具有挑战性的样式模式产生令人鼓舞的结果，从而保留了全球外观和精美的细节。此外，通过控制推理阶段的边缘数量，提出的方法还触发了新的功能，例如使用单个模型的多元化基于斑块的风格化。