Deep Supervised t-Distributed Embedding

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Grafit: Learning fine-grained image representations with coarse labels (2020, methodology): “Therefore, inspired by the neighborhood component analysis [23, 39, 47], Wu et al.”
Metric Learning with Adaptive Density Discrimination (2015, background): “…loss for a pair of triplets “symmetrized” for the two positive examples: Neighbourhood Components Analysis Neighbourhood Components Analysis (NCA) and its extensions (Goldberger et al., 2004; Salakhutdinov & Hinton, 2007; Min et al., 2010) have been designed in a similar spirit to Magnet Loss.”
Embedding with Autoencoder Regularization (2013, methodology): “Second, in [14] the stacked restricted Boltzmann machines (RBMs) are pre-trained and then fine-tuned with the supervised embedding constraints.”
Deep Unsupervised Similarity Learning Using Partially Ordered Sets (2017, background): “Further efforts [28, 19] have tried to reduce the computational cost of performing all pairwise comparisons [17].”