In this research, we aimed to investigate the use of a regression model with attention mechanisms for image classification tasks. Specifically, we explored the use of gradients with respect to the output scalar to understand how the attention weights are distributed across different layers of the model. Our findings reveal that these gradients emphasize the attribution of relevant information at each layer to a larger positive output, which can be useful for model explainability.
Gradient-based Attention
To better understand the attention mechanism in our regression model, we used a modified attention rollout approach [44]. This method enables us to analyze the attention weights associated with the global classification token (CLS) both horizontally and vertically, and then average them. We reshaped the weights to a squared matrix and used bilinear interpolation to resize them to the input dimension of 518 × 518 pixels.
Our results show that the attention weights are distributed across different layers of the model in a way that emphasizes the attribution of relevant information to a larger positive output. This can be visualized as a series of concentric circles, where each circle represents a different layer of the model, and the size of each circle is proportional to the importance of that layer in the overall classification task.
Interpreting Attention Weights
To interpret the attention weights, we can use an analogy with a teacher grading a test. Imagine that the input images are like students taking a test, and the teacher is using the attention mechanism to assign grades to each student based on their performance. The attention weights can be thought of as the "grade distribution" for each student, indicating how well they performed in different areas of the test.
By analyzing the gradients with respect to the output scalar, we can see that the attention weights are distributed across different layers of the model in a way that emphasizes the attribution of relevant information to a larger positive output. This means that the model is focusing on the most important features when making predictions, and the attention weights provide a way to understand which features are most critical for each prediction.
Conclusion
In conclusion, our research demonstrates the use of gradients with respect to the output scalar to analyze the attention mechanism in a regression model with attention mechanisms. We found that these gradients emphasize the attribution of relevant information at each layer to a larger positive output, which can be useful for model explainability. By interpreting the attention weights using everyday language and engaging metaphors, we can gain a better understanding of how the model is making predictions and what features are most critical for each prediction. This can help improve the trustworthiness and transparency of the model, which is essential in many applications of machine learning.
