2. Schematic representation of the Skewencoder architecture, a multitask learning framework based on an autoencoder with a single bottleneck neuron in the latent space. This design compresses high-dimensional data into projections on a one-dimensional manifold for skewness calculation. In our approach, the input dataset is divided into two parts. Data from the global (incremental) dataset (red path) contributes to the original autoencoder loss function (i.e., reconstruction MSE loss), which minimizes the difference between the inputs and the decoder's output. Data from the local dataset (blue path) contributes to a custom loss function based on the skewness of the data in the latent space.