Convolutional Dynamic Alignment Networks for Interpretable Classifications (2104.00032v2)

Abstract: We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA-Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which linearly transform their input with weight vectors that dynamically align with task-relevant patterns. As a result, CoDA-Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions. Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns. These model-inherent decompositions are of high visual quality and outperform existing attribution methods under quantitative metrics. Further, CoDA-Nets constitute performant classifiers, achieving on par results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet.

Overview of Convolutional Dynamic Alignment Networks for Interpretable Classifications

The paper introduces Convolutional Dynamic Alignment Networks (CoDA-Nets), a novel family of neural network models designed to improve the interpretability of classification tasks while maintaining competitive performance. CoDA-Nets address the ongoing challenge in deep learning of understanding and explaining model decisions. The central innovation in CoDA-Nets is the Dynamic Alignment Unit (DAU), which enables the network to compute input-dependent linear transformations aligned with task-relevant patterns.

Key Contributions

1. Dynamic Alignment Units (DAUs): CoDA-Nets are built around DAUs, which create weights dynamically aligned with discriminative input patterns, allowing for an interpretable linear decomposition of the output. This approach bridges the gap between the interpretability of linear models and the complex capabilities of neural networks.
2. Model-Inherent Explanations: CoDA-Nets provide their own explanations for model predictions through contribution maps, which highlight the input areas most influential in making a decision. These maps outperform traditional attribution methods in quantitative assessments.
3. Competitive Performance: Despite a focus on interpretability, CoDA-Nets achieve classification accuracies comparable to models such as ResNet and VGG on datasets like CIFAR-10 and TinyImagenet, demonstrating that transparency does not require sacrificing accuracy.

Methodology and Results

The paper details how CoDA-Nets employ a series of input-dependent linear transformations facilitated by DAUs. Each DAU transforms its input linearly, with its weight matrix dynamically aligning with relevant input features. This alignment optimizes the network to focus on task-relevant parts of the data, resulting in high-quality contribution maps naturally embedded within the model's architecture.

CoDA-Nets outperform existing attribution methods, as shown by evaluations against localization and pixel-removal metrics. These methods are typically computationally expensive as post-hoc explanations, whereas CoDA-Nets integrate explanation into their internal mechanics, offering efficiency and accuracy.

By adjusting parameters such as temperature scaling in their training regime, the authors demonstrate that interpretability can be controlled and enhanced without manual feature engineering or separate model approximations.

Broader Implications

CoDA-Nets signify a step forward in designing inherently interpretable neural architectures, an essential development for applications in fields requiring transparency and trust, such as healthcare and autonomous systems. This architectural paradigm could inspire further explorations into dynamic, self-explaining AI models that emphasize clear decision-making processes, enabling broader adoption of AI systems in critical domains.

Future Directions

The research opens pathways for integrating more complex interpretability mechanisms directly into neural architectures. As CoDA-Nets scale with optimized implementations, there is potential for deploying these models in larger-scale applications, including real-time scenarios where interpretability and speed are critical. Moreover, the interplay between interpretability and other neural network features like robustness and transferability presents a promising area for further investigation.

In conclusion, this paper positions CoDA-Nets as both a feasible and effective solution for interpretable AI, blending performance with transparency, and setting a foundation for future progress in self-explaining machine learning systems.