ELM: Embedding and Logit Margins for Long-Tail Learning. (arXiv:2204.13208v1 [cs.LG])

Long-tail learning is the problem of learning under skewed label
distributions, which pose a challenge for standard learners. Several recent
approaches for the problem have proposed enforcing a suitable margin in logit
space. Such techniques are intuitive analogues of the guiding principle behind
SVMs, and are equally applicable to linear models and neural models. However,
when applied to neural models, such techniques do not explicitly control the
geometry of the learned embeddings. This can be potentially sub-optimal, since
embeddings for …

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