  Deep learning techniques are currently being investigated for high energy
physics experiments, to tackle a wide range of problems, with quark and gluon
discrimination becoming a benchmark for new algorithms.
  One weakness is the traditional reliance on Monte Carlo simulations, which
may not be well modelled at the detail required by deep learning algorithms.
  The weakly supervised learning paradigm gives an alternate route to
classification, by using samples with different quark--gluon proportions
instead of fully labeled samples.
  The paradigm has, therefore, huge potential for particle physics
classification problems as these weakly supervised learning methods can be
applied directly to collision data.
  In this study, we show that realistically simulated samples of dijet and
Z+jet events can be used to discriminate between quark and gluon jets by using
weakly supervised learning.
  We implement and compare the performance of weakly supervised learning for
quark--gluon jet classification using three different machine learning methods:
the jet image-based convolutional neural network, the particle-based recurrent
neural network and the feature-based boosted decision tree.
