Harnessing Self-Supervision in Unlabelled Data for Effective World Representation Learning in AI Models
Swapnil Morandé · Qeios · 2023
Artificial intelligence (AI) models rely on large, labelled datasets to learn effective representations of the world. However, labelled data can be scarce, biased, and expensive to obtain. Self-supervised learning offers a promising solution by enabling models to learn from unlabelled data through pre-training tasks that involve predicting masked or distorted portions of the data. This allows the model to learn powerful representations without explicit human labelling. This conceptual research paper examines how self-supervision from unlabelled data can be harnessed to train AI models capable of learning richer, more meaningful representations of the world. A detailed methodology utilizing contrastive self-supervised learning on unlabelled images is proposed. Quantitative results demonstrate the proposed approach enables models to learn superior representations compared to supervised learning, particularly when labelled data is scarce. The research provides critical insights into the future promise of self-supervised learning in developing AI systems that better perceive and understand the complexity of the real world.