A Theory of Initialisation's Impact on Specialisation
Devon Jarvis Sebastian Lee Clémentine Carla Juliette Dominé Andrew M Saxe Stefano Sarao Mannelli
Prior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of catastrophic interference. This phenomenon is attributed to the network's tendency to reuse learned features across tasks. However, this explanation heavily relies on the premise that neuron specialisation occurs, i.e. the emergence of  localised representations. Our investigation challenges the validity of this assumption. Using theoretical frameworks for the analysis of neural networks, we show a strong dependence of specialisation on the initial condition. More precisely, we show that weight imbalance and high weight entropy can favour specialised solutions. We then apply these insights in the context of continual learning, first showing the emergence of a monotonic relation between task-similarity and forgetting in non-specialised networks. Finally, we show that  specialization by weight imbalance is beneficial on the commonly employed elastic weight consolidation regularisation technique.
machine learning theory, teacher student setup, initialisation, specialisation, statitistical mechanics of learning
We examine in theoretical frameworks how different initialisation schemes influence specialisation in neural networks and explore their impact on downstream tasks in settings such as continual learning.