Bias Free ReLU Network

Bias-free ReLU networks aim to mitigate biases inherent in neural networks, improving fairness and reliability in applications like image classification and quantitative MRI. Research focuses on understanding the limitations of bias-free architectures, particularly in their relationship to linear networks, and developing training strategies to minimize bias while maintaining accuracy, often employing modifications to standard loss functions or leveraging data augmentation techniques. These efforts are significant because reducing bias enhances the trustworthiness and ethical implications of AI models, leading to more robust and equitable outcomes across diverse applications.