Weakly Supervised Recovery of Semantic Attributes
This addresses the need for interpretable feature learning in weakly supervised settings, but it is incremental as it builds on existing methods with a hybrid approach.
The paper tackles the problem of extracting semantic attributes from data using only classification labels, proposing a neural network with discrete features followed by an MLP and decision tree to encourage semantic meaning. Results on benchmarks show improved feature extraction correlated with unseen attributes.
We consider the problem of the extraction of semantic attributes, supervised only with classification labels. For example, when learning to classify images of birds into species, we would like to observe the emergence of features that zoologists use to classify birds. To tackle this problem, we propose training a neural network with discrete features in the last layer, which is followed by two heads: a multi-layered perceptron (MLP) and a decision tree. Since decision trees utilize simple binary decision stumps we expect those discrete features to obtain semantic meaning. We present a theoretical analysis as well as a practical method for learning in the intersection of two hypothesis classes. Our results on multiple benchmarks show an improved ability to extract a set of features that are highly correlated with the set of unseen attributes.