Keywords: synthetic-to-real generalization, domain generalization
Abstract: 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 leverage 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. In addition, we enhance the proposed CSG framework with attentional pooling (A-pool) to let the model focus on semantically important regions and further improve its generalization. We demonstrate the effectiveness of CSG on various synthetic training tasks, exhibiting state-of-the-art performance on zero-shot domain generalization.
One-sentence Summary: We propose a new contrastive synthetic-to-real generalization framework that achieves state-of-the-art performance on synthetic-to-real generalization problems.
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Code: [![github](/images/github_icon.svg) NVlabs/CSG](https://github.com/NVlabs/CSG) + [![Papers with Code](/images/pwc_icon.svg) 1 community implementation](https://paperswithcode.com/paper/?openreview=F8whUO8HNbP)
Data: [COCO](https://paperswithcode.com/dataset/coco), [Cityscapes](https://paperswithcode.com/dataset/cityscapes), [GTA5](https://paperswithcode.com/dataset/gta5)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 2 code implementations](https://www.catalyzex.com/paper/arxiv:2104.02290/code)