& - & - & 64x64x1\
& Conv1 & 2 & 2 & 32x32x64\
& Conv2 & 2 & 2 & 16x16x128\
& Unfold & - & - & 32768\
& Bottleneck & - & - & 300\
& Dense2 & - & - & 32768\
&\

For both the 1D and 2D CAE, we use the Adam optimizer with learning rate 0.001, $\beta_1$ equal to 0.9 and mean squared error as the loss function. Additionally, both networks are trained for 10 epochs with batch size 200. For all convolutional and dense layers, a Leaky ReLU activation function is used with alpha equal to 0.3. The hyperparameters for the 1D network are obtained by following a grid search approach, while the hyperparameters for the 2D network are manually selected such that good reconstruction measures are observed for all types of encodings. Detailed information about the network architectures is given in Table \[tab:1d\_arch\].

Results
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We compare the different models based on the True and False Positive Rates (TPR, FPR), the F1-score, the Area Under the receiving operator Curve (AUC) and the time required to encode 10000 images. The performance metrics of all the selected setups are presented in Table \[tab:scores\].

