Can Biases in ImageNet Models Explain Generalization? (2404.01509v1)

Abstract: The robust generalization of models to rare, in-distribution (ID) samples drawn from the long tail of the training distribution and to out-of-training-distribution (OOD) samples is one of the major challenges of current deep learning methods. For image classification, this manifests in the existence of adversarial attacks, the performance drops on distorted images, and a lack of generalization to concepts such as sketches. The current understanding of generalization in neural networks is very limited, but some biases that differentiate models from human vision have been identified and might be causing these limitations. Consequently, several attempts with varying success have been made to reduce these biases during training to improve generalization. We take a step back and sanity-check these attempts. Fixing the architecture to the well-established ResNet-50, we perform a large-scale study on 48 ImageNet models obtained via different training methods to understand how and if these biases - including shape bias, spectral biases, and critical bands - interact with generalization. Our extensive study results reveal that contrary to previous findings, these biases are insufficient to accurately predict the generalization of a model holistically. We provide access to all checkpoints and evaluation code at https://github.com/paulgavrikov/biases_vs_generalization

• The paper finds that inherent shape, spectral, and critical band biases correlate with nuanced generalization across in-distribution and out-of-distribution data.
• The paper employs a fixed ResNet-50 architecture across 48 models with varied training methods like adversarial and contrastive training to isolate bias effects.
• The paper reveals that balanced shape-texture bias and bandwidth traits impact robustness differently, informing strategies for model design.

Analyzing the Role of Biases in ImageNet Models and Their Influence on Generalization

The paper, "Can Biases in ImageNet Models Explain Generalization?" presents a comprehensive investigation into the hypothesis that biases inherent in ImageNet-trained neural networks affect their ability to generalize to unseen data. The paper focuses on biases such as shape bias, spectral bias, and critical band properties, which distinguish machine vision from human perception. The authors leverage a fixed ResNet-50 architecture across 48 ImageNet models trained using diverse methodologies to ascertain if these biases serve as predictors for robust model generalization.

Study Context and Motivation

The robust generalization of neural networks to rare, in-distribution (ID) samples or out-of-distribution (OOD) samples is a critical challenge. Despite advances in ImageNet classification, these models exhibit vulnerabilities to adversarial attacks and shifts in data distribution, such as differing weather conditions or digital artifacts. The paper scrutinizes various biases—previously identified differences between model and human vision—and evaluates their correlation with generalization capabilities.

Methodological Framework

The analysis meticulously fixed the architecture to ResNet-50 to neutralize confounding variables from different architectural inductive biases. Various training methodologies, such as augmentation techniques, adversarial training, contrastive learning, and recent training recipes, were employed to train the models. The authors aimed to dissociate the effects of training methods from architectural differences on generalization. To measure biases, they examined shape bias using the cue-conflict dataset, spectral biases via bandpass-filtered ImageNet samples, and critical band properties using noise insertions on contrast-reduced and grayscale samples.

The paper also explored various facets of generalization through benchmarks on in-distribution datasets, robustness datasets, conceptual changes using sketches and stylized images, and adversarial robustness.

Key Findings and Insights

The paper elucidates several significant insights, challenging previous notions about these biases:

• Shape Bias: An inverse relationship was found between shape bias and ID performance. Models with stronger shape bias demonstrated lower performance on ID tasks. Moreover, adversarially trained models exhibited a balanced representation of shape and texture bias as optimal for robust performance.
• Spectral Bias: A weak correlation was observed between low-frequency bias and generalization performance, while a surprising positive correlation was identified between high-frequency biases and various generalization aspects, except for adversarial robustness.
• Critical Band: The critical band's bandwidth demonstrated correlations with robustness, yet the relationship was not directly causal. Models with narrower bandwidth showed better non-adversarial robustness, while broader bandwidths correlated with improved adversarial robustness, contrary to previous studies.

These findings highlight the complexity of generalization, suggesting that no single bias acts as a reliable predictor of generalization across diverse conditions. The discrepancies in correlations with adversarial training and across different training methodologies underscore the nuanced interaction between training, architectural design, and performance.

Implications and Future Directions

The inquiry into biases offers foundational insights with implications for the design of more robust AI models. It stresses the necessity of a holistic view, considering the multifaceted nature of generalization. As the authors advocate for enlarging methodological breadth, there lies a promising avenue for further research in devising mechanisms that accommodate bias alignment with human perception without compromising performance.

Future investigations could deepen understanding by exploring bias interactions and causal relationships in various architectures and implementing more comprehensive and adaptive benchmarks. The findings also emphasize the need for ongoing critical evaluations of models intended for safety-critical applications, where the cost of generalization failures can be substantial.

In conclusion, this paper adds invaluable nuance to the discourse surrounding model biases and their implications, foregrounding the complexity of model generalization. It raises salient considerations for AI development and evaluation and prompts reflective discourse on aligning machine learning models with human cognitive processes.