A Controlled Study of Feature-Based Knowledge Distillation Across Student Designs

arXiv:2608.08294 · cs.LG, cs.CV · Submitted 2026-08-08 · Read on arXiv

Abhinand Balachandran, Praveen Prashant

cs.LG, cs.CV

Submitted: 2026-08-08

Updated: 2026-08-11

Comments: 7 pages, 3 figures, 2 tables

Code: https://github.com/pytorch/vision

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 18× in this paper · explained in 1 episode
• Distillation — 14× in this paper · explained in 2 episodes
• Knowledge Distillation — 8× in this paper · explained in 15 episodes
• Attention Transfer — 6× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 3 episodes
• Attention Maps — 1× in this paper · explained in 2 episodes
• Augmentation — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Composition — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• In International Conference on Learning Representations — 1× in this paper
• Kullback–Leibler Divergence — 1× in this paper · explained in 2 episodes
• Model Compression — 1× in this paper · explained in 3 episodes
• Provenance — 1× in this paper · explained in 5 episodes
• Weight Decay — 1× in this paper · explained in 2 episodes

Sources

• Distilling the Knowledge in a Neural Network

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