Sensor-Conditioned Representation Learning via Scene-Relevant Observation Quotients

The paper introduces the Observation-Quotient Tucker-Structured Autoencoding (OQ-TSAE) framework, which focuses on sensor-conditioned representation learning by preserving scene distinctions while mitigating nuisance factors. It proposes the scene-relevant observation quotient as a target for representation correctness and demonstrates through experiments that quotient-consistent supervision enhances representation diagnostics compared to traditional methods. This approach emphasizes the need for evaluating sensor-conditioned representations not only on predictive performance but also on their ability to maintain the integrity of scene-relevant features, which is crucial for practitioners working with intelligent sensing systems.