Attention Transfer is a feature-based knowledge distillation method where the student is trained to mimic the teacher's spatial attention maps — ensuring the student focuses on the same image regions as the teacher, transferring "what to look at" rather than just "what to predict."

How Does Attention Transfer Work?

• Attention Map: $A = sum_c |F_c|^p$ where $F_c$ is the feature map of channel $c$ and $p$ controls the power.
• Loss: L2 distance between normalized teacher and student attention maps at each layer.
• Layers: Attention is transferred from multiple intermediate layers simultaneously.
• Paper: Zagoruyko & Komodakis, "Paying More Attention to Attention" (2017).

Why It Matters

• Interpretable: Directly transfers the spatial focus pattern from teacher to student.
• Complementary: Can be combined with logit-based distillation for stronger knowledge transfer.
• Efficiency: Small additional computational cost — attention maps are cheap to compute.

Attention Transfer is teaching the student where to look — transferring the teacher's spatial focus patterns to guide the student's feature learning.