신호학습연구실

We are a passionate team of researchers at Korea University, Republic of Korea, dedicated to advancing the frontiers of signal learning. Efficient and Robust Learning with Multiple Foundation Models for various deep learning tasks. Test-Time Adaptation (TTA) adapts a pre-trained model to new data distributions during inference, without accessing the original training data. Semantic Segmentation is the task of clustering parts of an image together which belong to the same object class. Continual Learning is a model learning a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks. Domain Adaption aims to adapt models from a labeled source domain to a different but target domain without labels. Knowledge Distillation extracts pivtoal knowledge from a teacher network to guide the learning of a student network.