Gauge theory and twins paradox of disentangled representations
This work addresses a theoretical problem for researchers in machine learning by providing a novel geometric interpretation of disentangled representations, though it appears incremental as it builds on existing discussions.
The paper tackles the problem of understanding disentangled representations in deep learning by developing a geometric framework that connects them to gauge theory and the twins paradox from relativity, which helps clarify conceptual issues about disentangled representations.
Achieving disentangled representations of information is one of the key goals of deep network based machine learning system. Recently there are more discussions on this issue. In this paper, by comparing the geometric structure of disentangled representation and the geometry of the evolution of mixed states in quantum mechanics, we give a fibre bundle based geometric picture of disentangled representation which can be regarded as a kind of gauge theory. From this perspective we can build a connection between the disentangled representations and the twins paradox in relativity. This can help to clarify some problems about disentangled representation.