Contrastive Syn-to-Real Generalization

Understand

Training on synthetic data can be beneficial for label or data-scarce scenarios.

• However, synthetically trained models often suffer from poor generalization in real domains due to domain gaps.
• In this work, we make a key observation that the diversity of the learned feature embeddings plays an important role in the generalization performance.
• To this end, we propose contrastive synthetic-to-real generalization (CSG), a novel framework that leverages the pre-trained ImageNet knowledge to prevent overfitting to the synthetic domain, while promoting the diversity of feature embeddings as an inductive bias to improve generalization.