Beyond Categorical Label Representations for Image Classification

TL;DR

This paper demonstrates that high-dimensional, high-entropy label representations can improve model robustness and data efficiency in image classification, challenging traditional categorical label approaches.

Contribution

It introduces the idea that complex, high-entropy label representations can enhance model robustness and training efficiency, supported by extensive experiments.

Findings

High-dimensional, high-entropy labels match categorical accuracy.

Features learned are more robust to adversarial attacks.

Better performance with limited training data.

Abstract

We find that the way we choose to represent data labels can have a profound effect on the quality of trained models. For example, training an image classifier to regress audio labels rather than traditional categorical probabilities produces a more reliable classification. This result is surprising, considering that audio labels are more complex than simpler numerical probabilities or text. We hypothesize that high dimensional, high entropy label representations are generally more useful because they provide a stronger error signal. We support this hypothesis with evidence from various label representations including constant matrices, spectrograms, shuffled spectrograms, Gaussian mixtures, and uniform random matrices of various dimensionalities. Our experiments reveal that high dimensional, high entropy labels achieve comparable accuracy to text (categorical) labels on the standard image classification task, but features learned through our label representations exhibit more robustness under various adversarial attacks and better effectiveness with a limited amount of training data. These results suggest that label representation may play a more important role than previously thought. The project website is at \url{https://www.creativemachineslab.com/label-representation.html}.