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About this paper
Jing Hu, Chengming Feng, Shu Hu, Ming-Ching Chang, Xin Li, Xi Wu, Xin Wang
RLMiniStyler introduces a reinforcement learning approach for arbitrary style transfer, enabling a single model to produce diverse stylization outputs with varying intensities without manual hyperparameter tuning. This lightweight framework generates sequential stylized images and achieves competitive performance with significantly fewer parameters than comparable methods.
Key insights
- Formulating style transfer as a sequential decision-making problem with reinforcement learning allows for automatic control over stylization intensity.
- A unified policy network for both content and style representation, combined with an Actor-Builder-Critic architecture, reduces model complexity and enhances efficiency.
- Uncertainty-aware multi-task learning and hierarchical style representation contrastive loss enable balanced content preservation and effective style transfer without manual tuning.
Summary by alphaXiv.
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