40 citations · 88 across the 11 of their papers we have counts for
Showing 2019Show all
2 papers · 1 filter
cs.CV2019★ 40 cited
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
Qing Lian, Fengmao Lv, Lixin Duan +1
We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic…
cs.CV2019★ 9 cited
MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation
Chaofan Tao, Fengmao Lv, Lixin Duan +1
How to effectively learn from unlabeled data from the target domain is crucial for domain adaptation, as it helps reduce the large performance gap due to domain shift or distributi…