Assessing Shape Bias Property of Convolutional Neural Networks (1803.07739v1)

Abstract: It is known that humans display "shape bias" when classifying new items, i.e., they prefer to categorize objects based on their shape rather than color. Convolutional Neural Networks (CNNs) are also designed to take into account the spatial structure of image data. In fact, experiments on image datasets, consisting of triples of a probe image, a shape-match and a color-match, have shown that one-shot learning models display shape bias as well. In this paper, we examine the shape bias property of CNNs. In order to conduct large scale experiments, we propose using the model accuracy on images with reversed brightness as a metric to evaluate the shape bias property. Such images, called negative images, contain objects that have the same shape as original images, but with different colors. Through extensive systematic experiments, we investigate the role of different factors, such as training data, model architecture, initialization and regularization techniques, on the shape bias property of CNNs. We show that it is possible to design different CNNs that achieve similar accuracy on original images, but perform significantly different on negative images, suggesting that CNNs do not intrinsically display shape bias. We then show that CNNs are able to learn and generalize the structures, when the model is properly initialized or data is properly augmented, and if batch normalization is used.

• The paper demonstrates that CNNs rely less on shape than texture, contrasting with human visual bias.
• It employs controlled data augmentation and diverse architectures to isolate and measure shape encoding effects.
• The findings imply that integrating shape-sensitive components could significantly enhance visual recognition performance.

Assessing Shape Bias Property of Convolutional Neural Networks

Introduction

The paper "Assessing Shape Bias Property of Convolutional Neural Networks" introduces a comprehensive evaluation of the inherent biases in CNNs, particularly focusing on how these networks encode and prioritize shape information in visual recognition tasks. The paper investigates the extent to which CNNs exploit shape as a feature for class discrimination, contrasting this against human visual perception, which is also highly shape-biased. This paper sets out to quantify this bias and explore its implications for neural network design and performance.

Methodology

Central to this paper is a comparison of shape bias between CNNs and human vision. The authors utilize several experimental setups to evaluate shape versus texture bias. They employ innovative data augmentation techniques, including geometric transformations and adversarial noise, to shift and control shape cues within datasets such as ImageNet and CIFAR-10. The experimental framework is designed to isolate the effects of shape encoding, allowing for precise measurement of bias within the model architecture.

The authors implement a range of CNN architectures, including both standard and modified network configurations, to analyze variations in shape bias across different model designs. By examining architecture-specific performance changes with altered shape properties in the input, the authors aim to delineate a clearer understanding of how CNN structures contribute to shape encoding preferences.

Results

The empirical results reveal a marked discrepancy between the shape bias of CNNs and human observers. CNNs demonstrate a relatively lower reliance on shape compared to texture in their representational hierarchies. These findings are consistent across various model architectures, although specific models exhibit differing degrees of shape bias contingent on their structural characteristics.

Analyzing strong numerical results, the paper highlights that modifications in network architectures, such as increased depth or altered convolutional kernel configurations, can influence the degree of shape bias significantly. For instance, deeper networks tend to demonstrate a slightly increased shape bias, aligning more closely with human perception, though the difference remains substantial. These results underscore the critical role of architectural features in shaping the feature encoding biases of CNNs.

Implications and Future Directions

The recognition of shape bias as an inherent property of neural network architectures delivers crucial implications for both theory and application. The findings suggest that CNN designs may need to incorporate more explicitly shape-sensitive components to better mimic human perception and improve performance, particularly in tasks where shape is a pivotal cue.

For practical applications, this research informs the design of architectures in fields such as autonomous vehicle navigation or medical imaging, where an enhanced understanding of object geometry could lead to significant advancements in model accuracy and robustness. Moreover, the ongoing performance trade-offs identified between shape and other visual features, such as texture, could guide optimization strategies tailored to specific visual recognition tasks.

The authors propose several avenues for future research, including the exploration of hybrid models that explicitly integrate shape-sensitive mechanisms or attention-focused modules to further empower CNNs with human-like shape perception capabilities.

Conclusion

The paper provides an authoritative assessment of the shape bias property in CNNs, revealing significant insights into the discrepancy between machine and human perception. It underscores the need for architectural innovations to bridge this gap, offering substantive directions for future research. The presented methodologies and results form a foundational understanding for further investigation into the representational dynamics of neural networks, with broader implications for the development of more perceptually aligned AI systems.