联合学习仅通过将本地模型更新传输到中央服务器来减轻分布式学习的隐私风险。但是，它面临着挑战，包括客户数据集的统计异质性以及客户设备的资源限制，这严重影响了培训性能和用户体验。先前的工作通过将个性化与模型压缩方案结合起来解决了这些挑战，包括量化和修剪。但是，修剪是数据依赖性的，因此必须在客户端进行，这需要相当大的计算成本。此外，修剪通常会在\ {0，1 \} $中训练二进制超级卸义$ \，这显着限制了模型容量，但没有计算益处。因此，培训需要高计算成本，并且需要很长时间才能收敛，而模型性能则没有回报。在这项工作中，我们提出了Hidenseek，该HIDENSEK在初始化时采用单次数据不合稳定的修剪来获得基于权重的突触显着性的子网。然后，每个客户端优化了\ { - 1，+1 \} $乘以未经修复的权重的标志Super-Mask $ \，以允许更快的收敛速度与最先进的压缩率相同。三个数据集的经验结果表明，与最先进的hidenseek相比，Hidenseek将推论精度提高了40.6 \％，同时将沟通成本和培训时间分别降低了39.7 \％和46.8％。

持续的学习方法通​​过试图解决灾难性遗忘来帮助深度神经网络模型适应和逐步学习。但是，无论这些现有方法是否传统上应用于基于图像的任务，都具有与移动或嵌入式传感系统生成的顺序时间序列数据相同的疗效仍然是一个未解决的问题。为了解决这一空白，我们进行了第一项全面的经验研究，该研究量化了三个主要的持续学习方案的性能（即，在三个移动和嵌入式感应应用程序中的六个数据集中的三个主要的持续学习方案（即正规化，重播和重播）的性能。不同的学习复杂性。更具体地说，我们在Edge设备上实现了端到端连续学习框架。然后，我们研究了不同持续学习方法的性能，存储，计算成本和记忆足迹之间的普遍性，权衡。我们的发现表明，以示例性计划（例如ICARL）重播，即使在复杂的场景中，甚至在复杂的场景中都具有最佳的性能权衡，以牺牲一些存储空间（少数MB）来训练示例（1％至5％）。我们还首次证明，以有限的记忆预算进行连续学习，可行和实用。特别是，两种类型的移动设备和嵌入式设备的延迟表明，可以接受递增的学习时间（几秒钟-4分钟）和培训时间（1-75分钟），可以接受，因为嵌入式嵌入式时可能会在设备上进行培训设备正在充电，从而确保完整的数据隐私。最后，我们为希望将不断学习范式应用于移动传感任务的从业者提供了一些准则。

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

学习在无人驾驶汽车（UAV）捕获的图像中检测物体（例如人类）通常会遭受无人机对物体的位置造成的巨大变化。此外，现有的基于无人机的基准数据集不提供足够的数据集元数据，这对于精确的模型诊断至关重要，并且学习功能不变。在本文中，我们介绍了大天使，这是第一个基于无人机的对象检测数据集，该数据集由具有相似想象条件以及无人机位置以及对象姿势元数据捕获的真实和合成子集组成。一系列实验经过精心设计，使用最先进的对象检测器设计，以证明在模型评估过程中利用元数据的好处。此外，还提供了几种涉及模型微调过程中涉及真实和合成数据的关键见解。最后，我们讨论了有关大天使的优势，局限性和未来方向，以突出其对更广泛的机器学习社区的独特价值。

由于存在浓烟或阴霾，从室外视觉环境收集的图像通常会降解。在这些退化的视觉环境（DVE）中，在场景理解中进行研究的关键挑战是缺乏代表性的基准数据集。这些数据集需要评估降级设置中的最新对象识别和其他计算机视觉算法。在本文中，我们通过引入带有朦胧和无雾图像的第一个配对的真实图像基准数据集以及原位的雾化密度测量来解决其中的一些限制。该数据集是在受控的环境中生产的，其专业烟雾产生机器覆盖了整个场景，并由从无人机（UAV）（UAV）和无人接地车（UGV）的角度捕获的图像组成。我们还评估了一组代表性的最先进的飞行方法以及数据集中的对象探测器。本文介绍的完整数据集，包括地面真相对象分类框和雾密度测量值，为社区提供了以下网址评估其算法的信息：https：//a2i2-archangel.vision。该数据集的一个子集已用于在CVPR UG2 2022挑战的雾痕中进行对象检测。

通用形态（UNIMORPH）项目是一项合作的努力，可为数百种世界语言实例化覆盖范围的标准化形态拐角。该项目包括两个主要的推力：一种无独立的特征架构，用于丰富的形态注释，并以各种语言意识到该模式的各种语言的带注释数据的类型级别资源。本文介绍了过去几年对几个方面的扩张和改进（自McCarthy等人（2020年）以来）。众多语言学家的合作努力增加了67种新语言，其中包括30种濒危语言。我们已经对提取管道进行了一些改进，以解决一些问题，例如缺少性别和马克龙信息。我们还修改了模式，使用了形态学现象所需的层次结构，例如多肢体协议和案例堆叠，同时添加了一些缺失的形态特征，以使模式更具包容性。鉴于上一个UniMorph版本，我们还通过16种语言的词素分割增强了数据库。最后，这个新版本通过通过代表来自metphynet的派生过程的实例丰富数据和注释模式来推动将衍生物形态纳入UniMorph中。

可靠的交通流量预测对于创建智能运输系统至关重要。已经开发出许多基于大数据的预测方法，但他们在考虑时间和地点的道路之间没有反映复杂的动态相互作用。在这项研究中，我们提出了一种动态定位的长短期记忆（LSTM）模型，涉及道路之间的空间和时间依赖。为此，我们使用局部动态空间权重矩阵以及其动态变化。此外，LSTM模型可以处理具有长依赖性的顺序数据以及复杂的非线性功能。经验结果表明，与两种不同的基线方法相比，所提出的模型的卓越预测性能。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.