  Autoencoders (AE) are simple yet powerful class of neural networks that
compress data by projecting input into low-dimensional latent space (LS).
Whereas LS is formed according to the loss function minimization during
training, its properties and topology are not controlled directly. In this
paper we focus on AE LS properties and propose two methods for obtaining LS
with desired topology, called LS configuration. The proposed methods include
loss configuration using a geometric loss term that acts directly in LS, and
encoder configuration. We show that the former allows to reliably obtain LS
with desired configuration by defining the positions and shapes of LS clusters
for supervised AE (SAE). Knowing LS configuration allows to define similarity
measure in LS to predict labels or estimate similarity for multiple inputs
without using decoders or classifiers. We also show that this leads to more
stable and interpretable training. We show that SAE trained for clothes texture
classification using the proposed method generalizes well to unseen data from
LIP, Market1501, and WildTrack datasets without fine-tuning, and even allows to
evaluate similarity for unseen classes. We further illustrate the advantages of
pre-configured LS similarity estimation with cross-dataset searches and
text-based search using a text query without language models.
