Innovations in Conditional Channel Gated Networks

This chapter examines the development of conditional channel gated networks, focusing on initial experiments and insights related to output distributions. It discusses a novel batch shaping technique and its implications for neural networks, addressing challenges like catastrophic forgetting in continual learning. The integration of gating mechanisms for filter selection in convolutional layers is explored, showcasing their potential to enhance model performance and manage task-specific learning.