Naturally Generated Decision Trees for Image Classification

Image classification has been a pivotal area of research in Deep Learning, with a vast body of literature working to tackle the problem, constantly striving to achieve higher accuracies. This push to reach achieve greater prediction accuracy however, has further exacerbated the black box phenomenon which is inherent of neural networks, and more for so CNN style deep architectures. Likewise, it has lead to the development of highly tuned methods, suitable only for a specific data sets, requiring significant work to alter given new data. Although these models are capable of producing highly accurate predictions, we have little to no ability to understand the decision process taken by a network to reach a conclusion. This factor poses a difficulty in use cases such as medical diagnostics tools or autonomous vehicles, which require insight into prediction reasoning to validate a conclusion or to debug a system. In essence, modern applications which utilize deep networks are able to learn to produce predictions, but lack interpretability and a deeper understanding of the data. Given this key point, we look to decision trees, opposite in nature to deep networks, with a high level of interpretability but a low capacity for learning. In our work we strive to merge these two techniques as a means to maintain the capacity for learning while providing insight into the decision process. More importantly, we look to expand the understanding of class relationships through a tree architecture. Our ultimate goal in this work is to create a technique able to automatically create a visual feature based knowledge hierarchy for class relations, applicable broadly to any data set or combination thereof. We maintain these goals in an effort to move away from specific systems and instead toward artificial general intelligence (AGI). AGI requires a deeper understanding over a broad range of information, and more so the ability to learn new information over time. In our work we embed networks of varying sizes and complexity within decision trees on a node level, where each node network is responsible for selecting the next branch path in the tree. Each leaf node represents a single class and all parent and ancestor nodes represent groups of classes. We designed the method such that classes are reasonably grouped by their visual features, where parent and ancestor nodes represent hidden super classes. Our work aims to introduce this method as a small step towards AGI, where class relations are understood through an automatically generated decision tree (representing a class hierarchy), capable of accurate image classification.