In my last blog post, I introduced my current research project: Learning to map raw input images into the shape space obtained from my prior study. Moreover, I talked a bit about the data set I used and the augmentation steps I took to increase the variety of inputs. Today, I want to share with you the network architecture which I plan to use in my experiments. So let’s get started.
In a previous mini-series of blog posts (see here, here, here, and here), I have introduced a small data set of 60 line drawings complemented with pairwise shape similarity ratings and analyzed this data set in the form of conceptual similarity spaces. Today, I will start a new mini-series about learning a mapping from images into these similarity spaces, following up on my prior work on the NOUN dataset (see here and here).
Since this is going to be my last blog post for this year, I’m going to use it for reflecting a bit on my academic life this year and for talking a bit about my plans for 2021.
This post will be the last one in my mini-series “A Similarity Space for Shapes” about joint work with Margit Scheibel. So far, I have described the overall data set, the correlation between distances and dissimilarities, and the well-shapedness of conceptual regions. Today, I will finally take a look at interpretable directions in this similarity space.
Today, I would like to continue my little series about recent joint work with Margit Scheibel on a psychologically grounded similarity space for shapes. In my first post, I outlined the data set we worked with, and in my second post, we investigated how well the dissimilarity ratings are reflected by distances in the similarity spaces. Today, I’m going to use the categories from our data set to analyze whether conceptual regions are well-formed.