  A cross domain multistream classification is a challenging problem calling
for fast domain adaptations to handle different but related streams in
never-ending and rapidly changing environments. Notwithstanding that existing
multistream classifiers assume no labelled samples in the target stream, they
still incur expensive labelling cost since they require fully labelled samples
of the source stream. This paper aims to attack the problem of extreme label
shortage in the cross domain multistream classification problems where only
very few labelled samples of the source stream are provided before process
runs. Our solution, namely Learning Streaming Process from Partial Ground Truth
(LEOPARD), is built upon a flexible deep clustering network where its hidden
nodes, layers and clusters are added and removed dynamically in respect to
varying data distributions. A deep clustering strategy is underpinned by a
simultaneous feature learning and clustering technique leading to
clustering-friendly latent spaces. A domain adaptation strategy relies on the
adversarial domain adaptation technique where a feature extractor is trained to
fool a domain classifier classifying source and target streams. Our numerical
study demonstrates the efficacy of LEOPARD where it delivers improved
performances compared to prominent algorithms in 15 of 24 cases. Source codes
of LEOPARD are shared in \url{https://github.com/wengweng001/LEOPARD.git} to
enable further study.
