FROM TEXT CLASSIFICATION TO IMAGE CLUSTERING, PROBLEMS LESS OPTIMIZED

Abstract

Machine Learning is thriving. Every industry is using its techniques in some way to improve their efficiency and revenue. However, the focus on research is not divided equally between all of the different areas and problems that this field can tackle and analyze. Currently, Computer Vision is the one area that is being focused very extensively by researchers and companies alike, and as a result has seen an amazing boost in the recent years. This ranges from the well-known problems of classification that use discriminative models all the way to more novel problems that use generative models such as style transfer, super resolution, and description generation. Yet, some other problems have not been worked on nearly as much as of now. These problems include some Natural Language Processing tasks like Sentence Classification and even Computer Vision problems such as Image Clustering. Each of these tasks has their own set of difficulties and obstructions that need to be tackled before they can be researched properly and used in the industry which is a great driving force for research. Specifically, the case of clustering seems to be interesting to look into as more and more lable-less and unknown data is being generated every day without means to process and analyze them efficiently. We will discuss these problems that have been focused on less throughout the recent years.