SPARK: Spatial Policy-driven Adaptive Reinforcement learning for Knowledge distillation
Low-bit quantization enables deployment of image restoration (IR) networks on resource-constrained devices, but introduces rounding noise that disproportionately degrades high-frequency regions such as edges and fine textures. Existing knowledge distillation (KD) methods apply distillation signals uniformly across all spatial locations, overlooking the varying reconstruction difficulty across image regions. To address this, we propose SPARK (Spatial Policy-driven Adaptive Reinforcement Learning for Knowledge Distillation), a framework that adaptively allocates distillation effort using a lightweight reinforcement learning (RL) policy network. At each training step, a difficulty feature ex...
| Authors | Mohamed Jismy Aashik Rasool, Shabir Ahmad, Gisong Oh et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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