From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection
This work addresses the need for explainability in deep learning models for critical applications like security, but it is incremental as it adapts existing methods rather than introducing new ones.
The authors tackled the challenge of interpreting CNN+RNN models for time-series anomaly detection by adapting visualization techniques like saliency maps and Grad-CAM to handle temporal data, though no concrete performance numbers were provided.
Deep neural networks are highly effective in solving complex problems but are often viewed as "black boxes," limiting their adoption in contexts where transparency and explainability are essential. This lack of visibility raises ethical and legal concerns, particularly in critical areas like security, where automated decisions can have significant consequences. The General Data Protection Regulation (GDPR) underscores the importance of justifying these decisions. In this work, we explore visualization techniques to improve the understanding of anomaly detection models based on convolutional recurrent neural networks (CNN + RNN) with a TimeDistributed layer. Our model combines VGG19 for convolutional feature extraction and a GRU layer for sequential analysis of real-time video data. While suitable for temporal data, this structure complicates gradient propagation, as sequence elements are processed independently, dissociating temporal information. We adapt visualization techniques such as saliency maps and Grad-CAM to address these challenges. This article highlights the difficulties in visually interpreting video-based models and demonstrates how techniques for static images can be adapted to recurrent architectures, offering a transitional solution in the absence of dedicated methods.