10 citations · 34 across the 14 of their papers we have counts for
5 papers · 1 filter
HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization
Sungyeon Kim, Boseung Jeong, Suha Kwak
Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning…
Cross-Domain Ensemble Distillation for Domain Generalization
Kyungmoon Lee, Sungyeon Kim, Suha Kwak
Domain generalization is the task of learning models that generalize to unseen target domains. We propose a simple yet effective method for domain generalization, named cross-domai…
Combating Label Distribution Shift for Active Domain Adaptation
Sehyun Hwang, Sohyun Lee, Sungyeon Kim +2
We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint. Inspired by recent a…
Self-Taught Metric Learning without Labels
Sungyeon Kim, Dongwon Kim, Minsu Cho +1
We present a novel self-taught framework for unsupervised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of…
Learning to Generate Novel Classes for Deep Metric Learning
Kyungmoon Lee, Sungyeon Kim, Seunghoon Hong +1
Deep metric learning aims to learn an embedding space where the distance between data reflects their class equivalence, even when their classes are unseen during training. However,…