Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentations

2025-03-05

AI Summary

This work investigates the internal inference mechanisms and parameter sensitivity of convolutional neural networks (CNNs) under concurrent multiple image augmentations. Method: We propose a controlled–comparative interpretability framework featuring a novel activation variance decomposition technique under multi-parameter input perturbations, integrating Sobol indices and Shapley values to quantify individual augmentation contributions to neuron activations and generate guidance-aware activation masks. Additionally, we introduce a one-class sensitivity analysis that identifies critical sensitive neurons via prediction deviation, enabling fine-grained sensitivity localization and visualization of cross-layer/channel functional specialization. Contribution/Results: Our approach significantly enhances CNN interpretability with respect to augmentation operations and establishes a cross-domain methodological bridge between AI interpretability research and computational modeling of biological neural systems.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Drawing parallels with the way biological networks are studied, we adapt the treatment--control paradigm to explainable artificial intelligence research and enrich it through multi-parametric input alterations. In this study, we propose a framework for investigating the internal inference impacted by input data augmentations. The internal changes in network operation are reflected in activation changes measured by variance, which can be decomposed into components related to each augmentation, employing Sobol indices and Shapley values. These quantities enable one to visualize sensitivity to different variables and use them for guided masking of activations. In addition, we introduce a way of single-class sensitivity analysis where the candidates are filtered according to their matching to prediction bias generated by targeted damaging of the activations. Relying on the observed parallels, we assume that the developed framework can potentially be transferred to studying biological neural networks in complex environments.

Problem

• Investigates internal inference changes in CNNs due to image augmentations.
• Proposes a framework using Sobol indices and Shapley values for sensitivity analysis.
• Explores potential transferability of the framework to biological neural networks.

Innovation

• Adapts treatment-control paradigm for explainable AI
• Uses Sobol indices and Shapley values for sensitivity analysis
• Introduces single-class sensitivity analysis for prediction bias