Unsupervised Learning

This term, I really wanted to present this one ICASSP paper I found very interesting, but then I realized that the authors followed up their work with another journal paper (IEEE TIP), so I decided to discuss both. Both of these papers deal with the topic of unsupervised image segmentation: Kanezaki, “Unsupervised Image Segmentation by Backpropagation”, ICASSP 2018 [URL]. Kim et al. “Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering”, IEEE Transactions on Image Processing, 2020 [URL]. Here are the slides I made to present this topic in our reading group: ...

The authors highlight the multiple shortcomings of the contemporary learning based image registration methods, such as the inaccuracy of the correspondences provided for training (especially when the deformed subject image is significantly different from the template image), the difficulty of incorporating new image features for learning without repeating the whole training procedure all over again, and the lack of variation in the training image features primarily because of the prohibitive computational cost associated with it. Moreover, the authors note that the best features are ``often learnt only at the template space", meaning if the template image is changed, the whole training procedure has to be re-done. ...