How Do Training Methods Influence the Utilization of Vision Models?

Summary

This paper explores how different training methods affect which parts of vision models are most important for making decisions, especially in image classification tasks.

What's the problem?

In neural networks, not all parts contribute equally to how decisions are made. Some layers can be changed or even reset without affecting the model's performance much. This raises the question: how does the way we train these models influence which layers are critical for their success? Understanding this could help improve how we design and train models for tasks like image classification.

What's the solution?

The authors conducted experiments on various image classification models using a dataset called ImageNet-1k. They kept the model architecture and training data the same but changed the training methods to see how that impacted which layers were important. They found that different training approaches affected the importance of different layers. For instance, better training methods increased the significance of earlier layers while sometimes neglecting deeper layers. Conversely, some training methods, like adversarial training, showed different trends.

Why it matters?

This research is important because it provides insights into how to train vision models more effectively. By understanding which training methods highlight certain layers, researchers can optimize models for better performance in real-world applications, such as medical imaging or autonomous vehicles, where accuracy is crucial.