ImageNet-D: Benchmarking Neural Network Robustness with Diffusion Synthetic Object

Core Concepts

Diffusion models generate diverse synthetic images to test neural network robustness, significantly reducing accuracy.

Abstract

Introduction

Neural networks' importance in various applications.
Synthetic images proposed for robustness evaluation.

Related Work

Robustness of neural networks explored in previous studies.
Need for test sets covering different factors for evaluation.

ImageNet-D

Creation using diffusion models for object recognition tasks.
Diffusion models' success in image generation tasks.

Experiments

Various models evaluated on ImageNet-D benchmark.
Significant accuracy drop observed across models.
Data augmentation methods evaluated for robustness improvement.

Conclusion

ImageNet-D establishes a rigorous benchmark for visual perception robustness.
Effectiveness in evaluating model robustness demonstrated.

Stats

Experimental results show ImageNet-D reduces accuracy by up to 60%.
ImageNet-D serves as an effective tool for assessing model robustness.

Quotes

"Our work suggests that diffusion models can be an effective source to test vision models."
"ImageNet-D significantly decreases the accuracy of various models, demonstrating the effectiveness in model evaluation."

Key Insights Distilled From

ImageNet-D

by Chenshuang Z... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18775.pdf

Deeper Inquiries

How can diffusion-generated images enhance model robustness as training samples?

Diffusion-generated images can enhance model robustness as training samples by providing diverse object and nuisance pairs that challenge the model's ability to generalize. These images, created using diffusion models, offer a wide range of backgrounds, textures, and materials that may not be easily accessible in natural datasets. By training on diffusion-generated images, models are exposed to a broader spectrum of visual variations, leading to improved generalization and robustness. Additionally, the controlled generation process allows for the creation of hard images that can effectively test the model's resilience to challenging scenarios, ultimately enhancing its robustness.

What are the implications of ImageNet-D's effectiveness in evaluating neural network robustness?

The effectiveness of ImageNet-D in evaluating neural network robustness has significant implications for the field of computer vision. By introducing a rigorous benchmark like ImageNet-D, researchers and practitioners can accurately assess the robustness of vision models across various object and nuisance combinations. The ability of ImageNet-D to significantly decrease the accuracy of models, including state-of-the-art ones like CLIP, LLaVa, and MiniGPT-4, highlights its importance in identifying common failures in vision models. This benchmark provides a standardized and challenging test set that can reveal weaknesses in models and guide improvements in their design and training strategies.

How does ImageNet-D compare to natural test sets in failure transferability?

ImageNet-D demonstrates comparable failure transferability to natural test sets, as evidenced by its ability to achieve similar accuracy to ImageNet (Failure) when evaluating shared failures of surrogate models. The synthetic images in ImageNet-D effectively capture the failures of neural networks, showcasing their ability to challenge models in a manner similar to natural images. This suggests that ImageNet-D can serve as a reliable benchmark for evaluating model robustness, offering a cost-effective and scalable alternative to traditional natural datasets. The results indicate that synthetic images like those in ImageNet-D can effectively identify model failures and provide insights into improving neural network performance.