In the past two blog posts, we have discussed both transfer learning and multi-task learning for a four-dimensional target space of shapes. We observed that sketch-based networks seem to work better than photograph-based networks and that multi-task learning gave better results than transfer learning. Today, we’ll try to see whether these results also generalize to target spaces of different dimensionality. Continue reading “Learning To Map Images Into Shape Space (Part 7)”
Category: Conceptual spaces
Learning To Map Images Into Shape Space (Part 6)
Last time, we talked about the transfer learning results. Today, it’s time to take a look at the multi-task learning approach. Let’s get started. Continue reading “Learning To Map Images Into Shape Space (Part 6)”
Learning To Map Images Into Shape Space (Part 5)
After having spent already four (!) blog posts on the general and specific setup for my current machine learning study (data set, architecture, experimental setup, and sketch classification), it is now finally time to reveal the first results of the mapping task with respect to the shape domain. Today, we’ll focus on the transfer learning setup before looking into the multi-task learning results in one of the next posts.
Continue reading “Learning To Map Images Into Shape Space (Part 5)”
Learning To Map Images Into Shape Space (Part 4)
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)”
Learning To Map Images Into Shape Space (Part 3)
It’s been a while since my last blog post on this subject. The reason for that is simply that the neural network did not give me the results I wanted. But now it seems that I’m on a better track, so let me give you a quick update on what has changed and an overview of my next steps.
Continue reading “Learning To Map Images Into Shape Space (Part 3)”