Full-Gradient Representation for Neural Network Visualization (1905.00780v4)

Abstract: We introduce a new tool for interpreting neural net responses, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity components. This is the first proposed representation which satisfies two key properties: completeness and weak dependence, which provably cannot be satisfied by any saliency map-based interpretability method. For convolutional nets, we also propose an approximate saliency map representation, called FullGrad, obtained by aggregating the full-gradient components. We experimentally evaluate the usefulness of FullGrad in explaining model behaviour with two quantitative tests: pixel perturbation and remove-and-retrain. Our experiments reveal that our method explains model behaviour correctly, and more comprehensively than other methods in the literature. Visual inspection also reveals that our saliency maps are sharper and more tightly confined to object regions than other methods.

• The paper establishes that achieving both completeness and weak dependence in saliency techniques is theoretically impossible, motivating the full-gradient approach.
• The paper introduces full-gradients by combining input sensitivity and neuron bias to accurately recover convolutional network outputs.
• Experiments show that FullGrad outperforms state-of-the-art methods in pinpointing crucial regions, enhancing neural network interpretability for practical applications.

Full-Gradient Representation for Neural Network Visualization

The paper presents an innovative approach to neural network interpretability known as full-gradients, introducing a representation that captures both input sensitivity and individual neuron sensitivity. This methodology addresses key gaps in current interpretability techniques by providing a representation that meets two critical properties: completeness and weak dependence. These properties have long been sought in saliency-based methods but have provably been impossible to achieve simultaneously. FullGrad is introduced as an approximation specifically for convolutional networks, constructed by aggregating components of the full-gradient.

Contributions and Numerical Results

The research stands out for its detailed theoretical contributions and rigorous empirical validation:

1. Theoretical Foundation: The paper establishes the incompatibility of completeness and weak dependence within saliency-based methods, thus arguing against their simultaneous satisfaction. This assertion is backed by theoretical proofs within the field of piecewise-linear models.
2. Introduction of Full-Gradients: The full-gradient representation, discussed in Section~\ref{sec:full-grad-repr}, assigns importance to both inputs and neuron-level features. The representation elegantly integrates both input and neuron bias components, ensuring the recovery of the neural network function output.
3. Comparative Experiments: Through experiments involving pixel perturbation and remove-and-retrain procedures, FullGrad demonstrates superior performance in explaining neural network behavior compared to several state-of-the-art methods. Specifically, FullGrad's effectiveness is quantitatively validated by outperforming existing methods in two key measures:
   • Pixel Perturbation: The method showed minimal change in output, indicating accurate identification of unimportant regions.
   • ROAR Evaluation: FullGrad resulted in the greatest accuracy reduction when applied to retrained models, confirming its ability to pinpoint crucial regions accurately.

Implications and Future Developments

The paper's findings have significant implications both theoretically and practically in the field of AI and neural network visualization. Theoretically, the introduction of full-gradients addresses the ongoing challenge of simultaneously handling local and global attributions in neural networks, presenting a novel representation that accounts for intrinsic biases and neuron interactions.

Practically, the methodology enhances the interpretability of models without compromising on the quality of the attribution. This has direct applications in areas where model transparency is crucial, such as medical imaging or autonomous vehicles where understanding decision boundaries is imperative.

However, the paper also highlights the key remaining challenge: the lack of standardized evaluation metrics for interpretability methods. This presents an opportunity for future research to create robust, domain-specific evaluation methods that could serve as benchmarks for assessing interpretability across varying contexts.

Conclusion

The full-gradient representation for neural network visualization offers a significant improvement in model interpretability. By addressing both input and neuron-level biases, FullGrad contributes to a more nuanced understanding of neural network decision-making processes. The research paves the way for advancements in the transparent deployment of AI systems, making it a valuable addition to the field of neural network interpretability. Going forward, establishing comprehensive benchmarks and refining post-processing techniques may further enhance the efficacy and applicability of such representations in diverse AI applications.