CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced
  Semi-Supervised Learning
Hyuck Lee Heeyoung Kim
Pseudo-label-based semi-supervised learning (SSL) algorithms trained on a
class-imbalanced set face two cascading challenges: 1) Classifiers tend to be
biased towards majority classes, and 2) Biased pseudo-labels are used for
training. It is difficult to appropriately re-balance the classifiers in SSL
because the class distribution of an unlabeled set is often unknown and could
be mismatched with that of a labeled set. We propose a novel class-imbalanced
SSL algorithm called class-distribution-mismatch-aware debiasing (CDMAD). For
each iteration of training, CDMAD first assesses the classifier's biased degree
towards each class by calculating the logits on an image without any patterns
(e.g., solid color image), which can be considered irrelevant to the training
set. CDMAD then refines biased pseudo-labels of the base SSL algorithm by
ensuring the classifier's neutrality. CDMAD uses these refined pseudo-labels
during the training of the base SSL algorithm to improve the quality of the
representations. In the test phase, CDMAD similarly refines biased class
predictions on test samples. CDMAD can be seen as an extension of post-hoc
logit adjustment to address a challenge of incorporating the unknown class
distribution of the unlabeled set for re-balancing the biased classifier under
class distribution mismatch. CDMAD ensures Fisher consistency for the balanced
error. Extensive experiments verify the effectiveness of CDMAD.