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Do Sparse Subnetworks Exhibit Cognitively Aligned Attention? Effects of Pruning on Saliency Map Fidelity, Sparsity, and Concept Coherence

Created by
  • Haebom

Author

Sanish Suwal, Dipkamal Bhusal, Michael Clifford, Nidhi Rastogi

Outline

While the impact of neural network pruning on model performance is well known, its impact on model interpretability remains unclear. In this study, we investigate how fine-tuning after size-based pruning alters low-level importance maps and high-level concept representations. Using ResNet-18 trained on ImageNette, we compare the posterior interpretations of Vanilla Gradients (VG) and Integrated Gradients (IG) according to pruning levels, evaluating sparsity and fidelity. Furthermore, we apply CRAFT-based concept extraction to track changes in semantic consistency of learned concepts.

Takeaways, Limitations

Light to moderate pruning improves the focus and fidelity of the importance map while maintaining distinct and meaningful concepts.
Excessive pruning reduces the sparsity and conceptual consistency of the importance map by merging disparate features while maintaining accuracy.
Although pruning can shape internal representations into human-like attentional patterns, excessive pruning hinders interpretability.
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