These models usually define the task embedding based on the parameters of an architecture (neural network) after training on each task. In particular, the Fisher information matrix of network parameters can be employed to capture the task structure (task embedding). An example of an architecture-dependent distance function is Task2Vec, which utilizes a probe network to quantify distance. The probe network can be any deep learning architecture (e.g., convolutional neural networks (CNNs) or graph neural networks (GNNs)) and is trained on all source and target tasks. The Fisher information matrix of the optimal parameters (i.e., the parameters after model training) is used to extract task representations and quantify task similarity. Using this metric, tasks that are semantically similar in the computer vision domain have been categorized in the same group. However, these methods for quantifying task relatedness have some drawbacks: They depend on the architecture of the probe network, and changing the architecture can alter the task embeddings and, consequently, distance. Moreover, there is limited theoretical support and guarantees for this distance concept.