Make multiple choice questions about following text:    Skip-GANomaly. The first model is an unsupervised reconstructionbased approach, called Skip-GANomaly (Akçay et al., 2019). As an unsupervised model, it is trained on normal (undamaged) images only and tested on both normal and anomalous (damaged) images in order to reasonably determine the decision threshold for the anomaly score. The general architecture of Skip-GANomaly is similar to that of GANs, consisting of two competing neural networks. The first one is a generator network based on an autoencoder and the second one is a discriminator network with a similar structure as the encoder with additional layers added to determine a classification. Based on the GAN-like architecture, the generator tries to copy the original image as well as possible to its output. The discriminator’s task is to distinguish reconstructed images from the original ones. As the generator gets better at reconstructing, the discriminator must get better at continuing to classify the images correctly. However, the generator will fail at reconstructing an anomalous image making the discriminator determine it as reconstruction. The anomaly score is then calculated as the weighted sum of the reconstruction loss (L2-norm) and the latent-representation loss (L2-norm).
PaDiM. The second selected model is an unsupervised representationbased model, called PaDiM (Defard et al., 2020). Unlike Skip-GANomaly, PaDiM does not use the reconstruction loss of an image, but the similarity of embedding vectors. An embedding vector is equal to the concatenation of feature maps from different hidden layers of a CNN. For this purpose, PaDiM uses several pre-trained CNN backbone networks, such as ResNet18 (He et al., 2015), WideResNet50 (Zagoruyko & Komodakis, 2017), and EfficientNetB5 (Tan & Le, 2020). The training process of PaDiM differs from the training of a normal neural network in that no weights of the model are adjusted to minimize an error function during training. Instead, only the feature maps from the forward pass of the train images that go through the pre-trained CNN are used.
PatchCore. The third selected model is another unsupervised model following a representation-based approach, called PatchCore (Roth et al., 2021). As with PaDiM, a pre-trained CNN backbone network (WideResNet50) is used to extract embedding vectors. However, in contrast to PaDiM, the embedding vectors are first stored in a socalled memory bank. To reduce computational effort, the memory bank is subsampled by applying a 𝑘-center-greedy algorithm (Wolf, 2011). Since the subsampling is computationally expensive, the original memory bank is reduced in dimensionality by a random projection (Van Der Maaten et al., 2009). The result of this procedure is called embedding coreset. To determine a test image as normal or anomalous, its embedding vectors are extracted in the first step. Afterwards, 𝑘 distances to the vectors of the embedding coreset are calculated using the 𝑘-nearest neighbor algorithm (Cover & Hart, 1967). Given these 𝑘 distances, an anomaly score is calculated for each image path using the distance to the nearest neighbor weighted by the other 𝑘 distances.
Auto-Classifier. Besides all three unsupervised models, we also selected a supervised approach to include a baseline model for a competitive comparison. More specifically, we chose the Auto-Classifier model proposed by Lopes and Alexandre (2020), which combines a CNNfusion approach with an AutoML procedure. In the fusion approach, several pre-trained CNN architectures are retrained. After that, the test results of all CNN architectures are merged (‘‘fused’’) together, weighted by an evaluation metric (the better an individual model performs, the more weight their vote gets), yielding a single classification result, which can be interpreted as an anomaly score. Several CNNs from the VGG- and ResNet-families are used as backbones (i.e., VGG11, VGG16, VGG19, ResNet18, ResNet34, ResNet50, ResNet101). The second part of this approach (i.e., AutoML) optimizes the classification part of the best individual CNN backbone for the use case by replacing the last part of the classifier with a new one, determined by an automated machine learning procedure (LeDell & Poirier, 2020).