Beyond entropy, several other network properties can be leveraged to identify layers that can be safely linearized without significant performance degradation. Some of these properties include:
Activation Sparsity: Layers with sparse activations, where a significant portion of neurons remain inactive, can potentially be linearized without impacting performance significantly.
Gradient Magnitude: Neurons with low gradient magnitudes indicate that they have minimal impact on the network's learning process. Identifying and linearizing these neurons can help reduce the network's depth without compromising performance.
Feature Importance: Analyzing the importance of features learned by different layers can help identify redundant or less critical layers that can be linearized.
Critical Path Analysis: Understanding the critical path of computations in the network can help identify layers that contribute minimally to the final output, making them candidates for linearization.
Layer Interactions: Analyzing how information flows between layers and identifying layers with minimal interaction or influence on subsequent layers can guide the linearization process.
By considering a combination of these network properties along with entropy, we can develop a more comprehensive and robust method for identifying layers suitable for linearization.