22 citations · 23 across the 4 of their papers we have counts for
4 papers · 1 filter
Deep Representation Learning with an Information-theoretic Loss
Shin Ando
This paper proposes a deep representation learning using an information-theoretic loss with an aim to increase the inter-class distances as well as within-class similarity in the e…
Weak-supervision for Deep Representation Learning under Class Imbalance
Shin Ando
Class imbalance is a pervasive issue among classification models including deep learning, whose capacity to extract task-specific features is affected in imbalanced settings. Howev…
Deep Over-sampling Framework for Classifying Imbalanced Data
Shin Ando, Chun-Yuan Huang
Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as…
Discriminative Learning of the Prototype Set for Nearest Neighbor Classification
Shin Ando
The nearest neighbor rule is a classic yet essential classification model, particularly in problems where the supervising information is given by pairwise dissimilarities and the e…