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Summary

Simplicity bias (SB) in neural networks aids generalization. This study introduces a frequency-aware measure for SB in CLIP models, revealing its impact on various image classification tasks and guiding optimal model design.

Keywords:
Adversarial robustnessCLIP ModelsImage classificationOut-of-Distribution generalizationSimplicity bias

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Simplicity bias (SB) is crucial for neural network generalization but its real-world impact is unclear.
  • Existing complexity measures may inaccurately assess SB, conflating it with model expressivity.

Purpose of the Study:

  • To systematically investigate SB in CLIP models and its effect on image classification generalization.
  • To propose a novel frequency-aware measure for quantifying SB.
  • To evaluate the trade-offs of SB modulation on diverse generalization tasks.

Main Methods:

  • Theoretical analysis of complexity measures.
  • Development of a frequency-aware SB measure using 1-D interpolations in frequency-restricted input space.
  • Modulation of SB in CLIP models for zero-shot and fine-tuning evaluations on OOD, image corruption, and adversarial tasks.

Main Results:

  • The proposed measure reveals SB shifts model sensitivity towards low-frequency components, explaining generalization improvements.
  • Stronger SB enhances Out-of-Distribution (OOD) generalization.
  • Stronger SB correlates with decreased adversarial robustness.

Conclusions:

  • SB plays a significant role in the generalization capabilities of CLIP models.
  • A frequency-aware measure provides deeper insights into SB mechanisms.
  • Balancing SB is critical for optimizing model performance across different generalization scenarios, particularly OOD versus adversarial robustness.