Invertible Activation

Invertible activation functions are a key component in invertible neural networks (INNs), enabling the exact computation of likelihoods and facilitating tasks like probabilistic forecasting and generative modeling. Current research focuses on developing INN architectures, such as those based on matrix factorization (e.g., LU-Net) and incorporating invertible activations into existing models like variational autoencoders for improved performance in applications like image compression and time series analysis. This area is significant because INNs offer advantages in areas requiring precise likelihood calculations and reversible transformations, leading to more robust and efficient models for various applications.