RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning

Understand

Most existing Image-to-Image Translation (I2IT) methods generate images in a single run of a deep learning (DL) model.

• However, designing such a single-step model is always challenging, requiring a huge number of parameters and easily falling into bad global minimums and overfitting.
• In this work, we reformulate I2IT as a step-wise decision-making problem via deep reinforcement learning (DRL) and propose a novel framework that performs RL-based I2IT (RL-I2IT).
• The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image.