Neural Network Training With Debiased CAM Supervision
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Solution Overview
Problem
Existing Convolutional Neural Networks (CNN) models generate biased Class Activation Maps (CAMs) when faced with biased images, leading to deviated and unfaithful explanations that mislead users, particularly due to image blurring or color distortion, which current training methods fail to address effectively.
Innovation Solution
A multi-task DebiasedCNN model is developed, trained with self-supervised learning to transfer explanations from unbiased to biased images, incorporating a secondary CAM prediction task and a tertiary bias level regression task to generate faithful and human-relatable explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the neural network is trained on biased images to improve prediction performance, then the model can handle real-world biased data better, but the Class Activation Maps become deviated and unfaithful to user expectations
Solution Approach 1:
The training objective is segmented into two independent components: classification loss for prediction accuracy and CAM loss for explanation faithfulness. This allows the model to optimize both prediction performance and explanation quality separately, resolving the contradiction between handling biased data and maintaining faithful explanations.
Solution Approach 2:
The CAM loss function acts as a counterweight to the classification loss, preventing the model from over-optimizing for prediction accuracy at the expense of explanation faithfulness. By balancing these two opposing forces through weighted summation, the model achieves both reliable predictions and faithful explanations on biased data.
2Adaptability or versatility
If the model is fine-tuned on biased images to adapt to real-world conditions, then prediction accuracy improves, but the CAM explanations become more deviated and misleading
Solution Approach 1:
The adaptation process is segmented into classification task optimization and explanation quality maintenance. The dual-loss framework enables the model to adapt to biased images while preserving explanation accuracy by independently optimizing both objectives simultaneously.
Solution Approach 2:
The CAM loss provides feedback on explanation quality during training, allowing the model to adjust its features to maintain faithful explanations even when adapting to biased data distributions. This feedback mechanism prevents explanation degradation during the adaptation process.
3Productivity
If standard training methods are used to optimize prediction performance, then the model achieves better accuracy, but the CAM maps misalign with user expectations and relevant objects
Solution Approach 1:
The training objective is segmented into classification accuracy optimization and CAM alignment optimization. This segmentation allows the model to achieve high prediction accuracy while simultaneously maintaining precise pixel-level alignment between CAMs and relevant objects through independent loss function optimization.
Solution Approach 2:
The loss function parameters are changed to include both classification loss and CAM loss components. This parameter modification enables the training process to optimize for both prediction accuracy and explanation alignment, preventing the misalignment that occurs with standard training methods.
Data Source
AI summary
A method and a system for training a neural network. The method includes receiving, by a processing device, a training image, a reference label and a reference class activation map, the reference label and the reference class activation map associated with a corresponding unbiased image of the training image and generating, using the processing device, a class label and a class activation map based on the training image using the neural network. The method also includes calculating, using the processing device, a classification loss value based on differences between the reference label and the class label, and a class activation map loss value based on differences between the reference class activation map and the class activation map and updating, using the processing device, the neural network to minimise the classification loss value and the class activation map loss value to improve accuracy of the neural network in generation of the class label and the class activation map.