Take 5: Interpretable Image Classification with a Handful of Features
This addresses the need for human-understandable decisions in AI, particularly for fine-grained image classification, though it is incremental as it modifies existing deep learning architectures.
The paper tackles the problem of interpretability in deep neural networks for image classification by proposing a sparse, low-dimensional final decision layer, achieving 97% to 100% of baseline accuracy on four benchmark datasets while using only 5 out of 50 features per class.
Deep Neural Networks use thousands of mostly incomprehensible features to identify a single class, a decision no human can follow. We propose an interpretable sparse and low dimensional final decision layer in a deep neural network with measurable aspects of interpretability and demonstrate it on fine-grained image classification. We argue that a human can only understand the decision of a machine learning model, if the features are interpretable and only very few of them are used for a single decision. For that matter, the final layer has to be sparse and, to make interpreting the features feasible, low dimensional. We call a model with a Sparse Low-Dimensional Decision SLDD-Model. We show that a SLDD-Model is easier to interpret locally and globally than a dense high-dimensional decision layer while being able to maintain competitive accuracy. Additionally, we propose a loss function that improves a model's feature diversity and accuracy. Our more interpretable SLDD-Model only uses 5 out of just 50 features per class, while maintaining 97% to 100% of the accuracy on four common benchmark datasets compared to the baseline model with 2048 features.