DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction
This provides a complementary tool for researchers and practitioners to better understand and debug deep learning models, though it is incremental as it builds on existing interpretation methods.
The paper tackles the problem of interpreting complex deep neural networks by visualizing their classification boundaries and data in two dimensions using discriminative dimensionality reduction, enabling inspection of data properties like outliers and adversaries.
Machine learning algorithms using deep architectures have been able to implement increasingly powerful and successful models. However, they also become increasingly more complex, more difficult to comprehend and easier to fool. So far, most methods in the literature investigate the decision of the model for a single given input datum. In this paper, we propose to visualize a part of the decision function of a deep neural network together with a part of the data set in two dimensions with discriminative dimensionality reduction. This enables us to inspect how different properties of the data are treated by the model, such as outliers, adversaries or poisoned data. Further, the presented approach is complementary to the mentioned interpretation methods from the literature and hence might be even more useful in combination with those. Code is available at https://github.com/LucaHermes/DeepView .