Speakers
Description
The objective of this work is to show an analysis of the performance of a very well known Convolutional Neural Network applied to the classification of animals in the wild.
The interesting aspect of this application is that the set of images used have diverse characteristics from the training set of the ConvNet.
The analysis goes beyond the typical display of error rates, precision and recall, by being focused on describing situations where the behaviour of the ConvNet is more erratic than expected. For instance, whereas in some cases a misclassification can be explained by a lack of visual information or by the presence of confusing information, we show errors in situations where a correct classification is easily made by a human being.
The aim is to contribute to the discussion of whether current Artificial Neural Networks technologies are suitable to be used in applications with very open and noisy domains.
