UNSUPERVISED FEATURE ACQUISITION IN MULTIMODAL SYSTEMS: INTEGRATING CONTRASTIVE LEARNING WITH INTRINSIC MOTIVATION PROTOCOLS

Abstract

The rapid proliferation of high-dimensional data across diverse domains, from remote sensing to autonomous robotics, has outpaced the capacity for human annotation. Traditional supervised learning paradigms, while effective, remain inextricably bound to the availability of large-scale, labeled datasets—a constraint that is particularly acute in specialized fields such as fraud detection and environmental monitoring. This article presents the "Intrinsically Motivated Contrastive Framework" (IMCF), a novel methodology that synergizes contrastive learning algorithms with intrinsic motivation mechanisms derived from developmental robotics. By treating the feature extraction process as an exploration problem, the IMCF utilizes prediction error and information gain as internal reward signals, guiding the model to prioritize rare and complex data points without external supervision. We evaluate the framework across multiple domains, including large-scale scene recognition and satellite radar imagery for oil spill detection. Our analysis reveals that integrating intrinsic motivation significantly enhances representation learning in imbalanced datasets, outperforming standard contrastive baselines in few-shot transfer tasks. Furthermore, we demonstrate that this approach facilitates "far transfer" of learned features, enabling models trained on general scene databases to adapt rapidly to specific, unrelated tasks. These findings suggest that mimicking biological curiosity mechanisms is a viable pathway toward robust, unsupervised artificial intelligence.