A follow up to the previous paper! This time around Oscar is experimenting with synthetic data, of varying levels of realism, in an attempt to build a model that can generate realistic output (a re-view autoencoder), to improve confidence in its correctness when used for classification. Because SAS has a view direction (with what can be thought of as a shadow), and the data often has multiple views this model lets you merge all data and then pick the view direction when reconstructing the input, to get a novel view. The representation is then used to classify the existence, or not, and type, of munition.

Link to conference version (open access): Automatic recognition of underwater munitions from multi-view sonar surveys using semi supervised machine learning: a simulation study

Link to local version, in case the above breaks: Automatic recognition of underwater munitions from multi-view sonar surveys using semi supervised machine learning: a simulation study