Feature Wise Linear Modulation

Feature-wise Linear Modulation (FiLM) is a technique used to dynamically adjust the internal representations of neural networks, enabling more flexible and adaptive models. Current research focuses on applying FiLM within various deep learning architectures, such as convolutional and recurrent networks, to improve performance in diverse applications including audio processing (e.g., modeling audio effects, keyword spotting, and packet loss concealment) and image enhancement. This approach offers advantages in efficiency and accuracy, particularly for handling time-varying signals and complex data, leading to improved model performance in real-time and resource-constrained environments.