Propose an architecture that combines supervised and unsupervised learning components to achieve state-of-the-art results in this task. Explain how the choice of representation and classifier is influenced by the semi-supervised paradigm.

Develop a neural network with two branches: a supervised branch for labeled data, and an unsupervised branch for unlabeled data. The representation learned from unlabeled images can be used to augment the labeled dataset, improving robustness and accuracy. The choice of representation is critical in this setup, as it should capture meaningful features that generalize across both domains.