Knowledge Distillation Meets Reinforcement Learning: A Cluster-Driven Approach to Image Processing.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Knowledge distillation (KD) trains efficient models, but struggles with dynamic tasks.
• Reinforcement learning (RL) excels in adaptive learning but can be computationally intensive.
• Complex data distributions in remote sensing and medical imaging pose challenges for current models.

Purpose of the Study:

• To propose a novel two-stage framework, Knowledge Distillation with Reinforcement Learning (KDRL), for enhanced model adaptability and efficiency.
• To improve model performance on complex and heterogeneous data distributions.
• To establish a scalable design for efficient model training in resource-constrained environments.

Main Methods:

• A two-stage approach: supervised fine-tuning with logit/feature distillation, followed by RL refinement.
• RL stage uses confidence-based and cluster alignment rewards, dynamically reducing task loss reliance.
• Introduces auxiliary layers in the student encoder for feature alignment with teacher cluster centers.

Main Results:

• KDRL significantly boosts lightweight student model performance on remote sensing benchmarks (e.g., AID, RESISC45).
• Achieves state-of-the-art cross-modal retrieval on RSITMD and improved visual-grounding precision on DIOR-RSVG.
• Demonstrates superior performance and computational efficiency across diverse tasks, validating the scalable design.

Conclusions:

• The KDRL framework effectively combines KD and RL to address model efficiency and domain heterogeneity.
• The proposed method enhances feature learning robustness through auxiliary layers and cluster alignment rewards.
• KDRL offers practical benefits for real-world deployments by reducing missed targets and speeding up analyst search on resource-constrained platforms.