  Zooplankton images, like many other real world data types, have intrinsic
properties that make the design of effective classification systems difficult.
For instance, the number of classes encountered in practical settings is
potentially very large, and classes can be ambiguous or overlap. In addition,
the choice of taxonomy often differs between researchers and between
institutions. Although high accuracy has been achieved in benchmarks using
standard classifier architectures, biases caused by an inflexible
classification scheme can have profound effects when the output is used in
ecosystem assessments and monitoring.
  Here, we propose using a deep convolutional network to construct a vector
embedding of zooplankton images. The system maps (embeds) each image into a
high-dimensional Euclidean space so that distances between vectors reflect
semantic relationships between images. We show that the embedding can be used
to derive classifications with comparable accuracy to a specific classifier,
but that it simultaneously reveals important structures in the data.
Furthermore, we apply the embedding to new classes previously unseen by the
system, and evaluate its classification performance in such cases.
  Traditional neural network classifiers perform well when the classes are
clearly defined a priori and have sufficiently large labeled data sets
available. For practical cases in ecology as well as in many other fields this
is not the case, and we argue that the vector embedding method presented here
is a more appropriate approach.
