Reweighting neural network examples for robust object detection at sea. Issue 16 (16th April 2021) - Becktor, J.; Boukas, E.; Blanke, M.; Nalpantidis, L.
Abstract: Deep neural networks have had profound significance in addressing visual object detection and classification tasks. However, though with the caveat of needing large amounts of annotated training data. Furthermore, the possibility of neural networks overfitting to the biases and faults included in their respective datasets. In this work, methods for achieving robust neural networks, able to tolerate untrusted and possibly erroneous training data, are explored. The proposed method is shown to improve performance and help neural networks learn from untrusted data, provided a thoroughly annotated subset.