A Unifying Mutual Information View of Metric Learning: Cross-Entropy vs. Pairwise Losses

Malik Boudiaf (1) , Jérôme Rony (1) , Imtiaz Masud Ziko (1) , Ismail Ben Ayed (1) , Eric Granger (1) , Marco Pedersoli (1) , Pablo Piantanida (2) , Ismail Ben Ayed (1)

1 LIVIA - Laboratoire d'imagerie, de Vision et d'intelligence artificielle
2 L2S - Laboratoire des signaux et systèmes

Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper investigates anonymization methods based on representation learning and deep neural networks, and motivated by novel informationtheoretical bounds. We introduce a novel training objective for simultaneously training a predictor over target variables of interest (the regular labels) while preventing an intermediate representation to be predictive of the private labels. The architecture is based on three sub-networks: one going from input to representation, one from representation to predicted regular labels, and one from representation to predicted private labels. The training procedure aims at learning representations that preserve the relevant part of the information (about regular labels) while dismissing information about the private labels which correspond to the identity of a person. We demonstrate the success of this approach for two distinct classification versus anonymization tasks (handwritten digits and sentiment analysis).

Mots clés: Deep learning; Representation learning; Privacy; Anonymization; Information theory; Supervised feature learning; Adversarial neural networks; Image classification; Sentiment analysis; Metric Learning; Deep Learning; Information Theory

Domaines: Théorie de l'information [cs.IT]; Réseaux et télécommunications [cs.NI]; Théorie de l'information et codage [math.IT]; Statistiques [math.ST]

Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Ismail Ben Ayed, Eric Granger, et al.. A Unifying Mutual Information View of Metric Learning: Cross-Entropy vs. Pairwise Losses. 16th European Conference on Computer Vision (ECCV), Sep 2021, Glasgow (virtual), United Kingdom. pp.548-564, ⟨10.1007/978-3-030-58539-6_33⟩. ⟨hal-03351131v2⟩