Commonly used AI networks are very self-confident in their predictions, even when the evidence for a certain decision is dubious. The investigation of a deep learning model output is pivotal for understanding its decision processes and assessing its capabilities and limitations. By analyzing the distributions of raw network output vectors, it can be observed that each class has its own decision boundary and, thus, the same raw output value has different support for different classes. Inspired by this fact, we have developed a new method for out-of-distribution detection. The method offers an explanatory step beyond simple thresholding of the softmax output towards understanding and interpretation of the model learning process and its output. Instead of assigning the class label of the highest logit to each new sample presented to the network, it takes the distributions over all classes into consideration. A probability score interpreter (PSI) is created based on the joint logit values in relation to their respective correct vs wrong class distributions. The PSI suggests whether the sample is likely to belong to a specific class, whether the network is unsure, or whether the sample is likely an outlier or unknown type for the network. The simple PSI has the benefit of being applicable on already trained networks. The distributions for correct vs wrong class for each output node are established by simply running the training examples through the trained network. We demonstrate our OOD detection method on a challenging transmission electron microscopy virus image dataset. We simulate a real-world application in which images of virus types unknown to a trained virus classifier, yet acquired with the same procedures and instruments, constitute the OOD samples.

我们介绍了一种基于辐射场接近基于图像的3D重建的新方法。体积重建的问题被制定为非线性最小二乘问题，并且在不使用神经网络的情况下明确解决。这使得能够使用具有比通常用于神经网络的更高收敛速率的求解器，并且在收敛之前需要更少的迭代。使用体素网格表示卷，其中场景被环境映射的层次结构包围。这使得可以获得前景和背景的360 {\ DEG}场景的清洁重建。来自众所周知的基准套房的许多合成和实际场景是以最先进的方法的质量成功重建，但在显着降低的重建时期。