TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation

• URL: http://arxiv.org/abs/2111.15300v1
• Date: Tue, 30 Nov 2021 11:25:46 GMT
• Title: TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation
• Authors: Fengyi Shen, Akhil Gurram, Ahmet Faruk Tuna, Onay Urfalioglu, Alois Knoll
• Abstract summary: Key challenge is to learn domain-agnostic representation of the inputs in order to benefit from virtual data. We propose a novel trident-like architecture that enforces a shared feature encoder to satisfy confrontational source and target constraints simultaneously. We also introduce a novel training pipeline enabling self-induced cross-domain data augmentation during the forward pass.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation. From domain adaptation perspective, the key challenge is to learn domain-agnostic representation of the inputs in order to benefit from virtual data. In this paper, we propose a novel trident-like architecture that enforces a shared feature encoder to satisfy confrontational source and target constraints simultaneously, thus learning a domain-invariant feature space. Moreover, we also introduce a novel training pipeline enabling self-induced cross-domain data augmentation during the forward pass. This contributes to a further reduction of the domain gap. Combined with a self-training process, we obtain state-of-the-art results on benchmark datasets (e.g. GTA5 or Synthia to Cityscapes adaptation). Code and pre-trained models are available at https://github.com/HMRC-AEL/TridentAdapt

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