"Rethink Deep Learning with Invariance in Data Representation (Tutorial Proposal)"

Teaching neural networks to focus on what matters by understanding geometric patterns

This paper proposes integrating invariance principles into data representations for more robust and efficient AI systems, reviving geometric transformations in modern deep learning.

-----

https://arxiv.org/abs/2412.04858

🤔 Original Problem:

Deep learning models largely ignore invariance principles, leading to issues with robustness, interpretability, and efficiency in real-world applications.

-----

🔧 Solution in this Paper:

→ The paper traces how invariance principles evolved from hand-crafted representations to modern Geometric Deep Learning (GDL)

→ It analyzes symmetry priors across different eras of deep learning development

→ The solution combines knowledge-driven invariant representations with data-driven approaches

→ It proposes integrating geometric transformations like translation and rotation invariance into neural architectures

-----

💡 Key Insights:

→ Pre-deep learning era focused on hand-crafted geometric invariance but lacked scalability

→ Early deep learning ignored invariance except basic translation equivariance

→ Modern GDL bridges knowledge-driven and data-driven approaches

→ Invariance helps build more robust and interpretable AI systems

-----

📊 Results:

→ Improved robustness against adversarial attacks

→ Enhanced interpretability in complex tasks

→ Reduced computational costs for web applications

→ Better performance in resource-constrained environments