  Attention mechanisms have raised significant interest in the research
community, since they promise significant improvements in the performance of
neural network architectures. However, in any specific problem, we still lack a
principled way to choose specific mechanisms and hyper-parameters that lead to
guaranteed improvements. More recently, self-attention has been proposed and
widely used in transformer-like architectures, leading to significant
breakthroughs in some applications. In this work we focus on two forms of
attention mechanisms: attention modules and self-attention. Attention modules
are used to reweight the features of each layer input tensor. Different modules
have different ways to perform this reweighting in fully connected or
convolutional layers. The attention models studied are completely modular and
in this work they will be used with the popular ResNet architecture.
Self-Attention, originally proposed in the area of Natural Language Processing
makes it possible to relate all the items in an input sequence. Self-Attention
is becoming increasingly popular in Computer Vision, where it is sometimes
combined with convolutional layers, although some recent architectures do away
entirely with convolutions. In this work, we study and perform an objective
comparison of a number of different attention mechanisms in a specific computer
vision task, the classification of samples in the widely used Skin Cancer MNIST
dataset. The results show that attention modules do sometimes improve the
performance of convolutional neural network architectures, but also that this
improvement, although noticeable and statistically significant, is not
consistent in different settings. The results obtained with self-attention
mechanisms, on the other hand, show consistent and significant improvements,
leading to the best results even in architectures with a reduced number of
parameters.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning