Contrastive Explanations in Neural Networks (2008.00178v1)

Abstract: Visual explanations are logical arguments based on visual features that justify the predictions made by neural networks. Current modes of visual explanations answer questions of the form $Why \text{ } P?'$. These $Why$ questions operate under broad contexts thereby providing answers that are irrelevant in some cases. We propose to constrain these $Why$ questions based on some context $Q$ so that our explanations answer contrastive questions of the form $Why \text{ } P, \text{} rather \text{ } than \text{ } Q?'$. In this paper, we formalize the structure of contrastive visual explanations for neural networks. We define contrast based on neural networks and propose a methodology to extract defined contrasts. We then use the extracted contrasts as a plug-in on top of existing $`Why \text{ } P?'$ techniques, specifically Grad-CAM. We demonstrate their value in analyzing both networks and data in applications of large-scale recognition, fine-grained recognition, subsurface seismic analysis, and image quality assessment.

• The paper introduces a method for generating contrastive explanations by integrating Grad-CAM with gradient-based contrast to distinguish predictions from alternatives.
• The approach mathematically defines contrast as the gradient difference needed to shift a prediction from class P to class Q, offering quantifiable insights into decision boundaries.
• Empirical results validate the method in fine-grained recognition and image quality assessment tasks, underscoring its practical impact on model interpretability.

Contrastive Explanations in Neural Networks: An Analytical Overview

"Contrastive Explanations in Neural Networks" by Prabhushankar et al. introduces a notable advancement in the interpretability of neural networks, specifically in generating nuanced explanations for the network's decisions. This paper addresses a significant gap in existing neural network interpretability methods, which focus primarily on explaining predictions by answering questions like "Why P?". These traditional approaches often operate within broad contexts, which can result in answers that lack specificity or relevance in certain situations. The authors propose an innovative method for producing contrastive explanations to answer questions of the form "Why P rather than Q?".

Methodology and Implementation

The authors formalize contrastive visual explanations by embedding context into visual interpretations provided by networks. The cornerstone of this approach lies in constraining existing "Why P?" techniques using methods like Grad-CAM and extending them to answer contrastive questions by defining both an expected outcome $P$ and a contrasting class or outcome $Q$.

Contrast Definition and Extraction

The core proposition of this paper is the definition and extraction of contrast in neural networks. Contrast is articulated as the difference in network response that would cause a prediction to shift from the predicted class $P$ to the contrastive class $Q$. The contrastive components are defined mathematically as the gradients achieved by backpropagating a loss function relating $P$ and $Q$ through the network. This gradient quantifies the necessary change in the network's parameters to alter the decision boundary from $P$ to $Q$.

Contrastive Explanations via Grad-CAM

The methodology integrates these contrastive gradients into existing visual explanation frameworks, specifically Grad-CAM. By applying a visualization over neural activities in response to contrastive gradients, the authors extend Grad-CAM to produce heatmaps that highlight salient image features corresponding to the contrast between two classes or outcomes. Noteworthy is that the system does not require input modifications, keeping the integrity of the original examples intact while offering insight into the decision process.

Empirical Validation

The empirical evaluation demonstrates the utility and effectiveness of contrastive explanations across several applications, including large-scale recognition, fine-grained recognition, and image quality assessment. The method reliably highlights distinct attributes differentiating closely related classes in recognition tasks, such as distinguishing between similar vehicle models or geological structures in subsurface imaging.

For image quality assessment, the approach helps identify regions affecting perceived quality, thereby informing on what aspects of the image the network attributes its quality scores. This is particularly beneficial in understanding network behavior in objective quality evaluation tasks.

Implications and Future Directions

The implications of this work are profound for both practical applications and theoretical development within AI interpretability and transparency. By introducing contrastive methods, the research enhances not just interpretability but also the diagnostic capability of neural networks, offering deeper insights into how networks process information and make decisions.

Future developments may explore generalized frameworks extending contrastive explanations to a wider array of neural architectures and domains. Furthermore, studies might investigate optimization of computation in extracting contrastive explanations, given the increased computation cost associated with calculating and interpreting gradient information between multiple classes or outcomes.

In conclusion, contrastive explanations represent a significant augmentation to neural interpretability paradigms, providing contextually relevant insights that are crucial for a comprehensive understanding of neural network decisions. This work lays the groundwork for further innovations in AI transparency and the accountable deployment of neural networks across critical applications.