In my last blog post, I gave an overview of the experiments I intend to conduct. Before, I had described the data set and the network architecture. Today, I finally report the first results. However, instead of first talking about the baseline for the mapping task (as originally intended), I will start with the classification results. The reason for this is that it makes more sense to discuss the baseline and my own transfer learning results together, since it is then much easier to compare them. Before I can talk about my own transfer learning results, I however first need to introduce the classification network on which they are based. So let’s focus focus today on the classification results that I was able to obtain with respect to the sketch data sets.
Continue reading “Learning To Map Images Into Shape Space (Part 4)”