# Adapting Visualization Techniques for Time-Series Anomaly Detection: From Convolutional Neural Networks to Convolutional-Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2411.04707 · HTML: https://mlsanity.com/paper/2411.04707/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 21 of 21)

- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Understanding the Role of Individual Units in a Deep Neural Network](https://mlsanity.com/paper/2009.05041/) (2020)
- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [Interpretable Convolutional Neural Networks](https://mlsanity.com/paper/1710.00935/) (2017)
- [Efficient Saliency Maps for Explainable AI](https://mlsanity.com/paper/1911.11293/) (2019)
- [M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning](https://mlsanity.com/paper/2007.00453/) (2020)
- [Poly-CAM: High resolution class activation map for convolutional neural networks](https://mlsanity.com/paper/2204.13359/) (2022)
- [Class-Discriminative Attention Maps for Vision Transformers](https://mlsanity.com/paper/2312.02364/) (2023)
- [TimeREISE: Time-series Randomized Evolving Input Sample Explanation](https://mlsanity.com/paper/2202.07952/) (2022)
- [AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans](https://mlsanity.com/paper/2407.02418/) (2024)
- [Adaptive occlusion sensitivity analysis for visually explaining video recognition networks](https://mlsanity.com/paper/2207.12859/) (2022)

## Abstract (arXiv)

Deep neural networks achieve strong performance on complex tasks but are often regarded as "black boxes," which limits their adoption in domains where transparency is essential. This lack of interpretability raises ethical and legal concerns, particularly in sensitive applications such as security, where automated decisions can have serious consequences. The General Data Protection Regulation (GDPR) reinforces the need to justify decisions made by these systems. In this work, we investigate visualization techniques to improve the interpretability of anomaly detection models based on convolutional recurrent neural networks (CNN+RNN) with a TimeDistributed layer. Our architecture combines Visual Geometry Group 19 (VGG19) for feature extraction with a Gated Recurrent Unit (GRU) for sequential analysis of real-time video data. While this design is well suited for temporal inputs, the TimeDistributed layer complicates gradient propagation and weakens the link between spatial and temporal information, reducing the effectiveness of standard visualization methods. To address this challenge, we adapt techniques such as saliency maps and Gradient-weighted Class Activation Mapping (Grad-CAM) to models that incorporate a temporal dimension. Although dedicated visualization methods for such architectures remain limited, our study highlights both the difficulties and the potential of applying tools originally designed for static images to recurrent convolutional networks handling video sequences. This approach extends classical interpretation strategies to temporal models and provides an intermediate solution until more specialized methods are developed.