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Learning a better representation with neural networks is a challengingproblem, which was tackled extensively from different prospectives in the pastfew years. In this work, we focus on learning a representation that could beused for a clustering task and introduce two novel loss components thatsubstantially improve the quality of produced clusters, are simple to apply toan arbitrary model and cost function, and do not require a complicated trainingprocedure. We evaluate them on two most common types of models, RecurrentNeural Networks and Convolutional Neural Networks, showing that the approach wepropose consistently improves the quality of KMeans clustering in terms ofAdjusted Mutual Information score and outperforms previously proposed methods.

Forced to Learn: Discovering Disentangled Representations Without Exhaustive Labels