  Deep learning models have become increasingly useful in many different
industries. On the domain of image classification, convolutional neural
networks proved the ability to learn robust features for the closed set
problem, as shown in many different datasets, such as MNIST FASHIONMNIST,
CIFAR10, CIFAR100, and IMAGENET. These approaches use deep neural networks with
dense layers with softmax activation functions in order to learn features that
can separate classes in a latent space. However, this traditional approach is
not useful for identifying classes unseen on the training set, known as the
open set problem. A similar problem occurs in scenarios involving learning on
small data. To tackle both problems, few-shot learning has been proposed. In
particular, metric learning learns features that obey constraints of a metric
distance in the latent space in order to perform classification. However, while
this approach proves to be useful for the open set problem, current
implementation requires pair-wise training, where both positive and negative
examples of similar images are presented during the training phase, which
limits the applicability of these approaches in large data or large class
scenarios given the combinatorial nature of the possible inputs.In this paper,
we present a constraint-based approach applied to the representations in the
latent space under the normalized softmax loss, proposed by[18]. We
experimentally validate the proposed approach for the classification of unseen
classes on different datasets using both metric learning and the normalized
softmax loss, on disjoint and joint scenarios. Our results show that not only
our proposed strategy can be efficiently trained on larger set of classes, as
it does not require pairwise learning, but also present better classification
results than the metric learning strategies surpassing its accuracy by a
significant margin.
