Target Domain Data

Target domain data research focuses on adapting machine learning models trained on one dataset (the source domain) to perform well on a different, often unlabeled, dataset (the target domain). Current research emphasizes techniques like adversarial learning, self-supervised learning, and pseudo-labeling to bridge the "domain gap" between source and target data, often employing convolutional neural networks and generative models. This work is crucial for deploying machine learning models in real-world scenarios where labeled data is scarce or expensive, impacting fields like medical image analysis, speech recognition, and natural language processing. Addressing the challenges of negative transfer and noisy pseudo-labels remains a key focus.