Semi-Supervised Image Classification (Cold Start)

This is the same as the semi-supervised image classification task, with the key difference being that the labelled subset chosen needs to be selection in a class agnostic manner. This means that the standard practice in semi-supervised learning of using a random class stratified sample is "cheating" in this case, as class information is required for the whole dataset for this to be done. Rather, this challenge requires a smart cold-start or unsupervised selective labelling strategy to identify images that are most informative and result in the best performing models.