title: DHOG: Deep Hierarchical Object Grouping
abstract: Unsupervised learning of categorical representations using data augmentations appears to be a promising approach and has proven useful for finding suitable representations for downstream tasks. However current state-of-the-art methods require preprocessing (e.g. Sobel edge detection) to work. We introduce a mutual information minimization strategy for unsupervised learning from augmentations, that prevents learning from locking on to easy to find, yet unimportant, representations at the expense of more informative ones requiring more complex processing. We demonstrate specifically that this process learns representations which capture higher mutual information between augmentations, and demonstrate that these representations are better suited to the downstream exemplar task of clustering. We obtain substantial accuracy improvements on CIFAR-10, CIFAR-100-20, and SVHN.
authors: Luke Nicholas Darlow Amos Storkey
hIndex: Amos Storkey 41