CEB Improves Model Robustness

Area of Science:

• Machine Learning
• Computer Vision
• Artificial Intelligence

Background:

• Classifiers require robustness to input variations for reliable performance.
• Information Bottleneck (IB) aims to create compressed, predictive input representations.
• Scaling IB methods to large-scale image classification has been challenging.

Purpose of the Study:

• To demonstrate the scalability and effectiveness of Conditional Entropy Bottleneck (CEB) in large-scale image classification.
• To show that CEB can improve model robustness.
• To evaluate CEB's performance in conjunction with data augmentation.

Main Methods:

• Implementing Conditional Entropy Bottleneck (CEB) for image classification.
• Integrating CEB with standard data augmentation techniques.
• Evaluating model performance on various robustness benchmarks.

Main Results:

• CEB successfully scales to large-scale image classification tasks.
• CEB demonstrates improved model robustness.
• CEB is compatible with and enhances data augmentation procedures.

Conclusions:

• Conditional Entropy Bottleneck (CEB) offers a scalable and effective approach to enhance classifier robustness.
• CEB provides a practical strategy for improving deep learning model resilience in computer vision applications.