The research field of image translation with the aid of learning algorithms is improving at a fast speed. For example, this earlier technique would look at a large number of animal faces and could interpolate between them, or in other words, blend one kind of dog into another breed. It could even transform dogs into cats, or even further transform cats into cheetahs. The results were very good, but it would only work on the domains it was trained on i.e. it could only translate to and from species that it took the time to learn about.
The performance of deep learning models has improved significantly on several computer vision tasks, but yet supervised learning models rely on a large number of labeled images. We know how expensive it is to get high-quality annotations, and this motivates research in other directions, including Active Learning.
In the previous posts of the 'Clustering in ML Series', we learned about the Connectivity-Based Clustering methods in ML. In this part, we will discuss another couple of clustering methods, Distribution-Based Clustering, and Fuzzy Clustering.
In the previous posts of the 'Clustering in ML Series', we learned about the Density-Based Clustering methods in ML. In this part, we will discuss another clustering method, known as Connectivity-Based Clustering. It is also known as Hierarchical clustering.
In the previous post of the 'Clustering in ML Series', we looked at what clustering is and some of the different clustering methods in ML. In this part, we will discuss the very first type of clustering method, known as Centroid-based clustering.