Domain Adaptation

Semantic segmentation is a challenging problem due to pixel level annotations requirement. Deep Convolutional Neural Networks (DCNNs) are performing with tremendous results on Semantic Segmentation problem but there are still limitation of training data for real-time applications. Domain Adaptation of Semantic segmentation tries to adapt the target domain data distribution without knowing labels to effectively do semantic segmentation in real-time scenarios. Generative adversarial Networks are also incorporated to learn the distribution of both the source and target data simultaneously and minimize their difference.

MULTI-LEVEL SELF-SUPERVISED LEARNING FOR DOMAIN ADAPTATION

Most of the recent Deep Semantic Segmentation algorithms suffer from large generalization errors, even when powerful hierarchical representation models based on convolutional neural networks have been employed. This could be attributed to…

LEARNING FROM SCALE-INVARIANT EXAMPLES FOR DOMAIN ADAPTATION IN SEMANTIC SEGMENTATION

Self-supervised learning approaches for unsupervised domain adaptation (UDA) of semantic segmentation models suffer from challenges of predicting and selecting reasonable good quality pseudo labels. In this paper, we propose a novel approach…