In intelligent sensing systems, learned representations are often evaluated by reconstruction fidelity or downstream prediction accuracy, but these criteria do not specify which latent distinctions are justified by the sensing process. In sensor-conditioned environments, nuisance factors can change measurements without altering the scene, while distinct scenes may be indistinguishable under limited sensing capability. This paper formulates sensor-conditioned representation correctness as preserving sensing-supported scene distinctions while suppressing nuisance-induced and sensor-unsupported variation.
We introduce the scene-relevant observation quotient, a representation target induced by sensing-supported distinguishability after nuisance canonicalization, and develop Observation-Quotient Tucker-Structured Autoencoding (OQ-TSAE), a scene-nuisance factorized framework with diagnostics for false distinction, false merge, nuisance sensitivity, and latent ordering consistency. Experiments on a controlled benchmark show that quotient-consistent supervision improves representation-correctness diagnostics over reconstruction-oriented, metric-learning, and contrastive-learning baselines.
Sensitivity, perturbation, and ablation studies demonstrate the importance of quotient-aligned supervision, reliable quotient relations, and quotient geometry. Complementary real-radar experiments indicate that a reconstruction-only OQ-TSAE variant retains competitive downstream utility, robustness under observation degradation, and low seed-to-seed variability. These results suggest that sensor-conditioned representations should be evaluated not only by predictive utility but also by whether their latent geometry preserves sensing-justified scene distinctions.
Blogger's Review: This paper presents a novel approach to sensor-conditioned representation learning, effectively addressing the issue of nuisance factors in sensing by introducing the framework of observation quotients. It fills a significant gap in existing evaluation standards, holding important theoretical implications and practical prospects, particularly in the fields of intelligent sensing and robotics, potentially advancing related technologies further.