The Lifecycle Principle: Stabilizing Dynamic Neural Networks with State Memory
This addresses a critical problem in neural network regularization for researchers and practitioners, offering a novel solution to instability in dynamic architectures, though it is incremental as it builds on existing regularization methods.
The paper tackles training instability in dynamic neural networks where neurons are deactivated for long periods, by proposing the Lifecycle principle that uses state memory to restore neurons to their last effective state instead of random re-initialization. Experiments on image classification benchmarks show improved generalization and robustness, with ablation studies confirming the necessity of state memory for these gains.
I investigate a stronger form of regularization by deactivating neurons for extended periods, a departure from the temporary changes of methods like Dropout. However, this long-term dynamism introduces a critical challenge: severe training instability when neurons are revived with random weights. To solve this, I propose the Lifecycle (LC) principle, a regularization mechanism centered on a key innovation: state memory. Instead of re-initializing a revived neuron, my method restores its parameters to their last known effective state. This process preserves learned knowledge and avoids destructive optimization shocks. My theoretical analysis reveals that the LC principle smooths the loss landscape, guiding optimization towards flatter minima associated with better generalization. Experiments on image classification benchmarks demonstrate that my method improves generalization and robustness. Crucially, ablation studies confirm that state memory is essential for achieving these gains.