Reproduce “Developmental Symmetry-Loss: A Free-Energy Perspective on Brain-Inspired Invariance Learning” on OpenResearch

About this paper

Arif Dönmez

Developmental Symmetry-Loss is introduced as a differentiable algorithmic principle for self-organizing representation learning systems, operationalizing the alignment of internal models with environmental symmetries. It interprets learning as the iterative minimization of 'structural surprise' to foster stable, compositional, and interpretable representations, bridging concepts from computational neuroscience and geometric machine learning.

Key insights

• Learning invariances in biological and artificial systems can be conceptualized as an iterative process of aligning internal models with environmental symmetries by minimizing 'structural surprise'.
• Disentangled and compositional representations can emerge intrinsically through a progressive factorization of the representation manifold into independent symmetry components.
• The framework provides a unified geometric account where both unsupervised representation learning and supervised regression are viewed as problems of 'orbit alignment' and 'orbit completion'.

Summary by alphaXiv.