The figure shows the activation maps for baseline
and our models (A-GCA and P-GCA). In the first exaample, the baseline model had predicted the wrong answer and had
high uncertainty in prediction.
Our model gave a correct answer while also minimizing
the uncertainty (thus leading to an improved visual explanation).
Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering (VQA) task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold benefits:
- Improvement in obtaining the certainty estimates that correlate better with misclassified samples.
- Improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions.
The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a recipe for obtaining improved certainty estimates and explanation for deep learning models. We provide detailed empirical analysis for the visual question answering task on all standard benchmarks and comparison with state of the art methods.