Weakly Supervised Recovery of Semantic Attributes

TL;DR

This paper introduces a method for extracting semantic attributes from data using weak supervision, combining neural networks with decision trees to discover meaningful features aligned with human-understandable concepts.

Contribution

It proposes a novel training approach that integrates neural networks with decision trees to recover semantic attributes from classification labels alone.

Findings

Improved extraction of features correlated with unseen attributes

Theoretical analysis of the intersection of hypothesis classes

Effective on multiple benchmark datasets

Abstract

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.