Evaluating the visualization of what a Deep Neural Network has learned (1509.06321v1)

Abstract: Deep Neural Networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multi-layer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision given a new unseen data sample. Recently, several approaches have been proposed enabling one to understand and interpret the reasoning embodied in a DNN for a single test image. These methods quantify the ''importance'' of individual pixels wrt the classification decision and allow a visualization in terms of a heatmap in pixel/input space. While the usefulness of heatmaps can be judged subjectively by a human, an objective quality measure is missing. In this paper we present a general methodology based on region perturbation for evaluating ordered collections of pixels such as heatmaps. We compare heatmaps computed by three different methods on the SUN397, ILSVRC2012 and MIT Places data sets. Our main result is that the recently proposed Layer-wise Relevance Propagation (LRP) algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method. We provide theoretical arguments to explain this result and discuss its practical implications. Finally, we investigate the use of heatmaps for unsupervised assessment of neural network performance.

• The paper presents a novel evaluation framework using region perturbation to objectively assess DNN heatmaps.
• It compares three popular methods—LRP, sensitivity analysis, and deconvolution—across datasets with the AOPC metric.
• Results reveal that LRP produces more intuitive and effective heatmaps, enhancing model transparency for critical applications.

Evaluating the Visualization of What a Deep Neural Network Has Learned

The paper by Samek, Binder, Montavon, Bach, and Müller, titled "Evaluating the Visualization of What a Deep Neural Network has Learned," addresses the critical issue of understanding and interpreting the decisions made by Deep Neural Networks (DNNs). The authors present a comprehensive methodology to assess the quality of heatmaps that visualize the decision-making process of these networks.

Core Contributions

The primary contribution of the paper lies in introducing a novel and objective framework for evaluating heatmaps generated by DNNs. This methodology revolves around the concept of region perturbation and is designed to quantify the "importance" of individual pixels, visualizing this in the form of a heatmap. The authors compare heatmaps generated by three different methods: Layer-wise Relevance Propagation (LRP), sensitivity analysis based on partial derivatives, and the deconvolution approach. Their evaluation spans three substantial datasets—SUN397, ILSVRC2012, and MIT Places.

Methodology

1. Heatmaps Generation:
   • Sensitivity Analysis: Utilizes partial derivatives to determine pixel importance, focusing on small local changes.
   • Deconvolution: Employs a backpropagation-like process to reverse the operations of a convolutional network.
   • Layer-wise Relevance Propagation (LRP): Distributes the network’s output prediction back to the input features, ensuring relevance conservation.
2. Region Perturbation and Evaluation:
   • The authors introduce region perturbation as a technique to iteratively modify the image regions based on their importance scores. They define two primary processes:
     • Most Relevant First (MoRF): Perturbs the image by removing information from the most relevant regions progressively.
     • Least Relevant First (LeRF): Perturbs the image by removing information from the least relevant regions progressively.
   • The quality of heatmaps is evaluated using the Area Over the Perturbation Curve (AOPC), which measures the drop in classification performance as increasingly relevant regions are perturbed.

Results and Findings

Quantitative Analysis:

• The LRP method consistently outperforms sensitivity analysis and the deconvolution approach across all three datasets. This is evident from the AOPC values, which indicate that LRP heatmaps better capture the relevant features influencing the network's decision.
• The deconvolution method performs better than sensitivity analysis but falls short of LRP. Sensitivity analysis exhibits the poorest performance, particularly for images lying outside the data manifold of the training set, such as those in SUN397 compared to the MIT Places dataset.

Qualitative Assessment:

• LRP heatmaps exhibit lower complexity (measured by file size and entropy), indicating more precise and less noisy relevance regions. This is consistent with their quantitative superiority.
• The paper includes visual examples demonstrating that LRP provides more intuitive and interpretable heatmaps, aligning better with human intuition about relevant features in images.

Implications and Future Directions

The implications of this research are significant both practically and theoretically:

• Practical Relevance: The ability to generate high-quality heatmaps helps in visually interpreting what parts of an image are most influential for a DNN’s decision. This is crucial for applications in fields requiring transparency and trust, such as medical imaging and autonomous driving.
• Theoretical Impact: The framework and findings lay a foundation for future work in improving the interpretability and transparency of DNN models. Moreover, the correlation between heatmap quality and network performance suggests potential applications in automated performance assessment of networks.

The paper opens avenues for further exploration:

• Automated Evaluation Mechanisms: Integrating heatmap evaluation into the training process might lead to more interpretable models.
• Generalization Beyond Image Data: Extending heatmap methodologies to other data types (e.g., time-series, text) could enhance the transparency of various DNN applications.
• Refinement of Perturbation Methods: Investigating other perturbation techniques or refining current ones could further improve heatmap quality and evaluation robustness.

Conclusion

The paper by Samek et al. makes a substantial contribution to the field of neural network interpretability by providing a robust, quantitative evaluation framework for heatmaps. This work helps bridge the gap between the high performance of DNNs and their often criticized lack of transparency, thereby enhancing the practical applicability of these powerful models.