Shortcut Learning Susceptibility in Vision Classifiers

TL;DR

This paper investigates how different vision model architectures, including CNNs, MLPs, and ViTs, are susceptible to shortcut learning by systematically introducing artificial cues and analyzing their reliance on these shortcuts versus genuine features.

Contribution

The study provides a systematic evaluation of shortcut learning across various vision architectures using controlled datasets with artificial shortcuts, including qualitative analysis of internal representations.

Findings

CNNs at lower learning rates are less reliant on shortcuts.

ViTs without positional encodings tend to ignore actual image features in presence of shortcuts.

Models vary significantly in their susceptibility to shortcut learning depending on architecture and training parameters.

Abstract

Shortcut learning, where machine learning models exploit spurious correlations in data instead of capturing meaningful features, poses a significant challenge to building robust and generalizable models. This phenomenon is prevalent across various machine learning applications, including vision, natural language processing, and speech recognition, where models may find unintended cues that minimize training loss but fail to capture the underlying structure of the data. Vision classifiers based on Convolutional Neural Networks (CNNs), Multi-Layer Perceptrons (MLPs), and Vision Transformers (ViTs) leverage distinct architectural principles to process spatial and structural information, making them differently susceptible to shortcut learning. In this study, we systematically evaluate these architectures by introducing deliberate shortcuts into the dataset that are correlated with class labels both positionally and via intensity, creating a controlled setup to assess whether models rely on these artificial cues or learn actual distinguishing features. We perform both quantitative evaluation by training on the shortcut-modified dataset and testing on two different test sets-one containing the same shortcuts and another without them-to determine the extent of reliance on shortcuts. Additionally, qualitative evaluation is performed using network inversion-based reconstruction techniques to analyze what the models internalize in their weights, aiming to reconstruct the training data as perceived by the classifiers. Further, we evaluate susceptibility to shortcut learning across different learning rates. Our analysis reveals that CNNs at lower learning rates tend to be more reserved against entirely picking up shortcut features, while ViTs, particularly those without positional encodings, almost entirely ignore the distinctive image features in the presence of shortcuts.