Abstract

This paper proposes an interactive system called Andromeda that enables users to interact with machine learning models by sorting images in a reduced dimension plot. In our system, a dimension reduction algorithm projects the images into a 2D space representing similarities between the images based on visual features extracted by a deep neural network. With Andromeda, users can alter the projection by dragging a subset of the images into groups according to their domain expertise. The underlying machine learning model learns the new projection by optimizing a weighted distance function in the feature space, and the model re-projects the images accordingly. The users can explore multiple custom projections, and can export a model for future classification tasks. Our approach incorporates user preferences into machine learning model construction and allows reuse of pre-trained image processing models to accomplish new tasks based on user inputs. Using edamame pod images as an example, we transferred a maturity based model into a model that can classify number of seeds per pod to demonstrate the utility of our system.