title: Repairing Systematic Outliers by Learning Clean Subspaces in VAEs
abstract: Data cleaning often comprises outlier detection and data repair.
  Systematic errors result from nearly
  deterministic transformations that occur repeatedly in the data, 
  e.g. specific image pixels being set to default values or watermarks.
  Consequently, models with enough capacity easily overfit to these
  errors, making detection and repair difficult.
  Seeing as a systematic outlier is a combination of patterns of a clean instance
  and systematic error patterns, our main insight is that inliers can be
  modelled by a smaller representation (subspace) in a model than outliers.
  By exploiting this,
  we propose \emph{Clean Subspace Variational Autoencoder (CLSVAE)}, 
  a novel semi-supervised model for detection and automated repair of
  systematic errors.
  The main idea is to partition the latent space and model inlier and 
  outlier patterns separately.
  CLSVAE is effective with much less labelled data compared to previous related
  models, often with less than 2\% of the data.
  We provide experiments using three image datasets in scenarios with
  different levels of corruption and labelled set sizes, comparing to relevant baselines.
  CLSVAE provides superior repairs without human intervention, 
  e.g. with just 0.25\% of labelled data we see a relative error 
  decrease of 58\% compared to the closest baseline.
authors: Simao Eduardo Kai Xu Alfredo Nazabal Charles Sutton
hIndex: Charles Sutton 43