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Found 6 papers, 4 papers with code
Ensemble of Loss Functions to Improve Generalizability of Deep Metric Learning methods
The proposed ensemble of losses enforces the deep model to extract features that are consistent with all losses.
A Framework to Enhance Generalization of Deep Metric Learning methods using General Discriminative Feature Learning and Class Adversarial Neural Networks
To address this limitation, we propose a framework to enhance the generalization power of existing DML methods in a Zero-Shot Learning (ZSL) setting by general yet discriminative representation learning and employing a class adversarial neural network.
Low-Rank Robust Online Distance/Similarity Learning based on the Rescaled Hinge Loss
An important challenge in metric learning is scalability to both size and dimension of input data.
Sparse Bayesian approach for metric learning in latent space
Also, the present work is extended for learning in the feature space induced by an RKHS kernel.
Robust Metric Learning based on the Rescaled Hinge Loss
In this paper, we formulate the metric learning problem using the Rescaled Hinge loss function and then develop an efficient algorithm based on HQ (Half-Quadratic) to solve the problem.
Large Scale Local Online Similarity/Distance Learning Framework based on Passive/Aggressive
To the best of our knowledge, this is the first local online similarity/distance learning framework based on PA (Passive/Aggressive).
