In this work, we propose to use a bio-inspired architecture called Fully Recurrent Convolutional Neural Network(FRCNN) to solvethe separation task. This model containsbottom-up,top-downandlateral connections tofuse information processed atvarious time-scales represented by stages.

Text-to-image diffusion has attracted vast attention due to its impressive image-generation capabilities.

It has been a long-standing dream to design artificial agents that explore their environment efficiently viaintrinsic motivation, similar tohowchildren perform curious free play.

Most existing work in this area relies on non-probabilistic approaches or on complex Reinforcement Learning (RL)-based hard structures.