  We provide the first results from the complete SNAD adaptive learning
pipeline in the context of a broad scope of data from large-scale astronomical
surveys. The main goal of this work is to explore the potential of adaptive
learning techniques in application to big data sets. Our SNAD team used Active
Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates
in the photometric data from the first 9.4 months of the Zwicky Transient
Facility (ZTF) survey, namely, between March 17 and December 31 2018 (58194 <
MJD < 58483). We analysed 70 ZTF fields at a high galactic latitude and
visually inspected 2100 outliers. This resulted in 104 SN-like objects being
found, 57 of which were reported to the Transient Name Server for the first
time and with 47 having previously been mentioned in other catalogues, either
as SNe with known types or as SN candidates. We visually inspected the
multi-colour light curves of the non-catalogued transients and performed
fittings with different supernova models to assign it to a probable photometric
class: Ia, Ib/c, IIP, IIL, or IIn. Moreover, we also identified unreported
slow-evolving transients that are good superluminous SN candidates, along with
a few other non-catalogued objects, such as red dwarf flares and active
galactic nuclei. Beyond confirming the effectiveness of human-machine
integration underlying the AAD strategy, our results shed light on potential
leaks in currently available pipelines. These findings can help avoid similar
losses in future large-scale astronomical surveys. Furthermore, the algorithm
enables direct searches of any type of data and based on any definition of an
anomaly set by the expert.
