PURSUhInT: Knowledge Distillation Framework
In Search of Informative Hint Points Based on Layer Clustering
Overview
PURSUhInT introduces a novel approach to knowledge distillation by identifying informative hint points based on layer clustering, enabling efficient transfer of knowledge from large teacher models to smaller student models.
The framework achieves significant model compression (up to 2.5x) while maintaining minimal accuracy loss. By carefully selecting which intermediate representations to transfer, PURSUhInT outperforms traditional knowledge distillation methods on multiple benchmark datasets.
Methodology
(a) Feature Extraction
The framework begins by extracting features from input images through successive neural network layers. These layers progressively reduce spatial dimensions while increasing feature depth, creating rich representations that capture hierarchical patterns in the data.
(b) Hint Point Selection
Layer representations are clustered to identify the most informative features. The clustering algorithm groups similar layer activations, and from these clusters, specific hint points (HP1, HP2, HP3) are selected. These hint points represent the most valuable intermediate representations for knowledge transfer.
(c) Knowledge Distillation Training
The Student model is trained using three loss components: L_hint (matching intermediate features with Teacher), L_logit (matching final predictions with Teacher), and L_cls (matching predictions with ground truth). This multi-objective approach ensures the Student learns both from the Teacher's knowledge and the true labels.
PURSUhInT Framework
The PURSUhInT framework showing (a) feature extraction, (b) hint point selection through clustering, and (c) knowledge distillation training with Teacher-Student architecture.
Key Contributions
2.5x Model Compression
Achieve significant reduction in model size while preserving performance.
Layer Clustering
Novel clustering approach to identify the most informative layers for knowledge transfer.
Informative Hint Points
Automatic selection of optimal hint points for efficient knowledge distillation.
Minimal Accuracy Loss
Maintains high accuracy even with significant model compression.
Technologies Used
Publication
Expert Systems with Applications (Impact Factor: 8.5)
PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation. Volume 213, 2023.
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