DHOG: Deep Hierarchical Object Grouping

Understand

Recently, a number of competitive methods have tackled unsupervised representation learning by maximising the mutual information between the representations produced from augmentations.

• The resulting representations are then invariant to stochastic augmentation strategies, and can be used for downstream tasks such as clustering or classification.
• Yet data augmentations preserve many properties of an image and so there is potential for a suboptimal choice of representation that relies on matching easy-to-find features in the data.
• We demonstrate that greedy or local methods of maximising mutual information (such as stochastic gradient optimisation) discover local optima of the mutual information criterion; the resulting representations are also less-ideally suited to complex downstream tasks.