PhD Thesis: Methodologies for Concept Identification and Recovery in Convolutional Neural Networks

Overview

This repository contains the code and documentation related to my PhD thesis titled "Methodologies for Concept Identification and Recovery in Convolutional Neural Networks (CNNs)." The work focuses on enhancing the interpretability and reliability of CNNs through the development of novel methodologies for concept attribution and recovery.

Motivation

The rapid advancement of CNN architectures, such as AlexNet, VGG16, GoogLeNet, ResNet50, and Florence, has led to impressive performance in various image classification tasks. However, these models often act as "black boxes," making their decision-making processes opaque and raising concerns about their reliability and fairness.

General Objective:

Develop methodologies for identifying and recovering concepts in convolutional neural networks using attribution techniques to improve confidence in decisions made by the networks.

Specific Objectives:

1. Concept Attribution: Develop techniques to identify and attribute concepts within CNNs, improving the quality and robustness of explanations.
2. Concept Recovery: Design methodologies to recover original concepts from CNNs affected by adversarial attacks or noise, thereby enhancing prediction reliability.

Methodologies

Concept Identification

• Techniques: Implement and evaluate various attribution methods, such as LIME, SHAP, Grad-CAM, and Meaningful Perturbation (MP).
• Fidelity and Robustness: Assess the fidelity and robustness of these techniques using metrics like Intersection over Union (IoU) and Structural Similarity Index Measure (SSIM).

Toy example

• What does a CNN network understand about a cat?
• What significant concepts does the neural network encode about a cat?

The developed methodology highlights in a much more detailed heatmap than the state-of-the-art "Meaningful Perturbation" method, not just the cat's face as the most important concept of the "cat" category, but also the details, particularly the pointed ears and eye shape.

If the neural network focuses on the correct concept, it is a reliable neural network, validated under the particular concept, the methodology for identifying concepts validates the concepts and thus allows improving confidence in the decisions made by networks. It also enables identifying biases in datasets.

Concept Recovery

• Adversarial Robustness: Utilize adversarial training techniques to improve the model's resilience against adversarial samples.
• Optimization Algorithms: Develop algorithms that generate masks to identify and recover key attributes, restoring the model's original predictions.

Implementation

• Dataset: Experiments are conducted on standard datasets such as ImageNet.
• CNN Architectures: Various CNN models, including ResNeXt and FixResNeXt-101, are used to validate the methodologies.
• Metrics: Evaluation metrics include IoU, SSIM, Pearson HOG, and deletion scores.

Results

The proposed methodologies demonstrate significant improvements in both fidelity and robustness compared to baseline models. The developed techniques provide high-quality explanations and effective recovery of original predictions, enhancing the transparency and reliability of CNN-based systems.

Fidelity in an explanation refers to the ability to identify the relevance of a feature when removed from an image, which decreases its prediction probability. Methods with better fidelity are those that minimize the deletion score, which monitors the drop in classification probability of a convolutional neural network when pixels are deleted.

We managed to lower the deletion score by a factor of 3.68x, which means that the represented concept condenses with fewer pixels, indicating that the method is more selective and meaningful in selecting the pixels that conform to the category's concept being evaluated.

Conclusion

This work contributes to the field of AI interpretability by offering robust and reliable methodologies for concept identification and recovery in CNNs. Future research directions include exploring concept-based transfer learning and network compression guided by interpretability.

A family of methodologies for identifying concepts (MIC, MIC_blur, MIC_inp, and MIC_inp_tv) was developed by modifying Meaningful Perturbation and implementing a directory of corrections, to produce explanations with high fidelity and robustness that allow improving confidence, security, and transparency in decisions made by convolutional neural networks on images. Additionally, a concept recovery methodology (MRC) was developed, which generates a mask selecting representative attributes to recover predictions affected by noise or adversarial images using an optimization algorithm.

Repository Structure

• code/: Contains the implementation of the methodologies.
• data/: Includes sample datasets used for testing and evaluation.
• results/: Stores the results and evaluation metrics of the experiments.
• docs/: Documentation and additional resources related to the thesis.

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For any inquiries or collaborations, please contact Fabio Guzmán at fabioandres.guzman@gmail.com

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