Computer Vision – Next generation technology – Witan World

Training deep learning-based models relies on large annotated datasets, which requires lots of resources. Despite achieving state-of-the-art performance in many visual recognition tasks, cross-domain differences still constitute a big challenge. To transfer knowledge across domains, Maximum Classifier Discrepancy for Unsupervised Domain Adaptation uses a novel adversarial learning method for domain adaptation without a need for any labeling information from the target domain. It's observed that minimizing the discrepancy between the probability estimates from two classifiers for samples from a target domain can produce class-discriminative features for various tasks, from classification to semantic segmentation.