  The Dark Energy Survey is able to collect image data of an extremely large
number of extragalactic objects, and it can be reasonably assumed that many
unusual objects of high scientific interest are hidden inside these data. Due
to the extreme size of DES data, identifying these objects among many millions
of other celestial objects is a challenging task. The problem of outlier
detection is further magnified by the presence of noisy or saturated images.
When the number of tested objects is extremely high, even a small rate of noise
or false positives leads to a very large number of false detections, making an
automatic system impractical. This study applies an automatic method for
automatic detection of outlier objects in the first data release of the Dark
Energy Survey. By using machine learning-based outlier detection, the algorithm
is able to identify objects that are visually different from the majority of
the other objects in the database. An important feature of the algorithm is
that it allows to control the false-positive rate, and therefore can be used
for practical outlier detection. The algorithm does not provide perfect
accuracy in the detection of outlier objects, but it reduces the data
substantially to allow practical outlier detection. For instance, the selection
of the top 250 objects after applying the algorithm to more than $2\cdot10^6$
DES images provides a collection of uncommon galaxies. Such collection would
have been extremely time-consuming to compile by using manual inspection of the
data.
