In recent years, Computer Vision has been experiencing a strong development thanks to Convolutional Neural Networks (CNNs). Models trained in a supervised way on large datasets such as ImageNet have achieved state-of-the-art performance in several tasks. But, the potential of supervised training is limited by the need to have the images annotated within the datasets, which is a very expensive process. This problem is even more pronounced in scenarios where the type of images and thus the labels to be assigned to them are not known in advance. This thesis aims to find a way to annotate unlabelled datasets efficiently by using a cluster-based rather than an instance-based type of annotation.