Improving Representation Learning from Data and Model Perspectives: Semi-Supervised Learning and Foundation Models

years, yet major challenges remain, including the reliance on large
labeled datasets, the difficulty of learning from imperfect unlabeled
data, and the need for models that generalize across diverse tasks and
do-mains. This dissertation addresses these challenges from both data
and model perspectives.

The first part improves standard semi-supervised learning methods by
introducing new techniques that better utilize unlabeled data, including
adaptive pseudo-labeling and representation-based regularization. The
second part tackles more realistic learning scenarios, where unlabeled
data may be imbalanced, noisy, or domain-shifted, and proposes novel
algorithms and a new benchmark to support robust evaluation across
multiple domains. The final part takes a step toward general-purpose AI
by developing a diffusion-based model capable of handling multiple
vision tasks within a unified framework.

Together, these contributions aim to make AI systems more practical,
reliable, and versatile, advancing us closer to generalizable and
data-efficient learning.