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Conditional Similarity Networks

Abstract

What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately.

Authors

Andreas Veit, Serge Belongie, Theofanis Karaletsos

Conference

CVPR 2017

Full Paper

‘Conditional Similarity Networks’ (PDF)

Uber AI

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Theofanis took his first steps as a machine learner at the Max Planck Institute For Intelligent Systems in collaboration with Microsoft Research Cambridge with work focused on unsupervised knowledge extraction from unstructured data, such as generative modeling of images and phenotyping for biology. He then moved to Memorial Sloan Kettering Cancer Center in New York, where he worked on machine learning in the context of cancer therapeutics. He joined a small AI startup Geometric Intelligence in 2016 and with his colleagues formed the new Uber AI Labs. Theofanis' research interests are focused on rich probabilistic modeling, approximate inference and probabilistic programming. His main passion are structured models, examples of which are spatio-temporal processes, models of image formation, deep probabilistic models and the tools needed to make them work on real data. His past in the life sciences has also made him keenly interested in how to make models interpretable and quantify their uncertainty, non-traditional learning settings such as weakly supervised learning and model criticism.