Web: http://arxiv.org/abs/2205.01903

May 5, 2022, 1:12 a.m. | Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak

cs.LG updates on arXiv.org arxiv.org

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 an embedding model and learning the model with the
predicted relations as pseudo labels. At the heart of our framework lies an
algorithm that investigates contexts of data on the embedding space to predict
their class-equivalence relations as pseudo labels. The algorithm enables
efficient end-to-end training since it demands no off-the-shelf module for
pseudo labeling. Also, the …

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