The proposed method can indeed be extended to other types of neural network architectures beyond image classification, such as language models or graph neural networks. The key lies in adapting the entropy-based importance metric and the linearization process to suit the specific characteristics of these architectures. For language models, the metric can be tailored to capture the unique patterns and activations in text data, while for graph neural networks, it can be modified to account for the structural properties of graphs.
In language models, layers with low entropy in terms of word embeddings or contextual information can be identified for linearization. Similarly, in graph neural networks, layers with low entropy in terms of node or edge features can be targeted for reduction. By customizing the entropy metric and the linearization process to the specific requirements of these architectures, the method can effectively reduce the depth of neural networks while maintaining performance across a variety of tasks and domains.