  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 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.
