Semantically meaningful image manipulation often involves laborious manual human examination for each desired manipulation. Recent success suggests that leveraging the representation power of existing Contrastive Language-Image Pretraining (CLIP) …
In general, an experimental environment for deep learning assumes that the training and the test dataset are sampled from the same distribution. However, in real-world situations, a difference in the distribution between two datasets, domain shift, …
Recent self-supervised representation learning methods depend on joint embedding learning with siamese-like networks, trained by maximizing the agreement of differently augmented same-class representations (positive pairs). Using positive pairs may …