Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

1 code implementation • NeurIPS 2020 • Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, Jinwoo Shin

While semi-supervised learning (SSL) has proven to be a promising way for leveraging unlabeled data when labeled data is scarce, the existing SSL algorithms typically assume that training class distributions are balanced.

Pseudo Label