Out-of-Distribution (OoD) detection has developed substantially in the past
few years, with available methods approaching, and in a few cases achieving,
perfect data separation on standard benchmarks. These results generally involve
large or complex models, pretraining, exposure to OoD examples or extra
hyperparameter tuning. Remarkably, it is possible to achieve results that can
exceed many of these state-of-the-art methods with a very simple method. We
demonstrate that ResNet18 trained with Supervised Contrastive Learning (SCL)
produces state-of-the-art results out-of-the-box on near and far OoD detection
benchmarks using only Euclidean distance as a scoring rule. This may obviate
the need in some cases for more sophisticated methods or larger models, and at
the very least provides a very strong, easy to use baseline for further
experimentation and analysis.

使用 ResNet18 结合监督对比学习 (Supervised Contrastive Learning)，通过欧几里得距离作为评分规则，得到了针对近程和远程 Out-of-Distribution 检测基准的开箱即用的最先进结果，这可能消除了在某些情况下对更复杂方法或更大模型的需求，并且至少提供了一个非常强大且易于使用的基线供进一步实验和分析。

SupEuclid: 用监督对比学习和欧几里得距离实现极其简单、高质量的非样本外检测

SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised  Contrastive Learning and Euclidean Distance

Modern remote sensing image change detection has witnessed substantial
advancements by harnessing the potent feature extraction capabilities of CNNs
and Transforms.Yet,prevailing change detection techniques consistently
prioritize extracting semantic features related to significant
alterations,overlooking the viability of directly interacting with bitemporal
image features.In this letter,we propose a bitemporal image graph Interaction
network for remote sensing change detection,namely BGINet-CD. More
specifically,by leveraging the concept of non-local operations and mapping the
features obtained from the backbone network to the graph structure space,we
propose a unified self-focus mechanism for bitemporal images.This approach
enhances the information coupling between the two temporal images while
effectively suppressing task-irrelevant interference,Based on a streamlined
backbone architecture,namely ResNet18,our model demonstrates superior
performance compared to other state-of-the-art methods (SOTA) on the GZ CD
dataset. Moreover,the model exhibits an enhanced trade-off between accuracy and
computational efficiency,further improving its overall effectiveness

提出了一种基于图交互网络的双时相遥感变化检测方法，并采用 ResNet18 作为骨干网络，在 GZ CD 数据集上取得了优异的性能。

基于图交互的遥感图像变化检测

Remote Sensing Image Change Detection with Graph Interaction

Recently, vision transformers were shown to be capable of outperforming
convolutional neural networks when pretrained on sufficient amounts of data. In
comparison to convolutional neural networks, vision transformers have a weaker
inductive bias and therefore allow a more flexible feature detection. Due to
their promising feature detection, this work explores vision transformers for
tumor detection in digital pathology whole slide images in four tissue types,
and for tissue type identification. We compared the patch-wise classification
performance of the vision transformer DeiT-Tiny to the state-of-the-art
convolutional neural network ResNet18. Due to the sparse availability of
annotated whole slide images, we further compared both models pretrained on
large amounts of unlabeled whole-slide images using state-of-the-art
self-supervised approaches. The results show that the vision transformer
performed slightly better than the ResNet18 for three of four tissue types for
tumor detection while the ResNet18 performed slightly better for the remaining
tasks. The aggregated predictions of both models on slide level were
correlated, indicating that the models captured similar imaging features. All
together, the vision transformer models performed on par with the ResNet18
while requiring more effort to train. In order to surpass the performance of
convolutional neural networks, vision transformers might require more
challenging tasks to benefit from their weak inductive bias.

本研究探索了视觉 Transformer 在数字病理学全幻灯片图像中，用于四种组织类型的肿瘤检测和组织类型识别。实验结果表明，在大量未标注全幻灯片图像的预训练下，视觉 Transformer 在肿瘤检测方面比 ResNet18 表现稍好。

数字病理学中视觉 Transformer 和卷积神经网络的比较研究

A comparative study between vision transformers and CNNs in digital  pathology

While rule-based attribution methods have proven useful for providing local
explanations for Deep Neural Networks, explaining modern and more varied
network architectures yields new challenges in generating trustworthy
explanations, since the established rule sets might not be sufficient or
applicable to novel network structures. As an elegant solution to the above
issue, network canonization has recently been introduced. This procedure
leverages the implementation-dependency of rule-based attributions and
restructures a model into a functionally identical equivalent of alternative
design to which established attribution rules can be applied. However, the idea
of canonization and its usefulness have so far only been explored
qualitatively. In this work, we quantitatively verify the beneficial effects of
network canonization to rule-based attributions on VGG-16 and ResNet18 models
with BatchNorm layers and thus extend the current best practices for obtaining
reliable neural network explanations.

本文从定量的角度验证了网络规范化对基于规则的说明在带有 BatchNorm 层的 VGG-16 和 ResNet18 模型上的益处，从而扩展了获得可靠神经网络说明的当前最佳实践。