From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection

• URL: http://arxiv.org/abs/2411.04707v3
• Date: Tue, 24 Dec 2024 12:58:48 GMT
• Title: From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection
• Authors: Fabien Poirier,
• Abstract summary: Deep neural networks are highly effective in solving complex problems but are often viewed as "black boxes"<n>This article highlights the difficulties in visually interpreting video-based models and demonstrates techniques for static images can be adapted to recurrent architectures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Real-Time Anomaly Detection in Video Streams [0.0]
  This thesis is part of a CIFRE agreement between the company Othello and the LIASD laboratory. The objective is to develop an artificial intelligence system that can detect real-time dangers in a video stream.
  arXiv  Detail & Related papers  (2024-11-29T14:24:33Z)
• TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation [6.013821375459473]
  We introduce a novel deep learning architecture for medical image segmentation. Our proposed model shows consistent improvement over the state of the art on ten publicly available datasets.
  arXiv  Detail & Related papers  (2024-09-05T09:14:03Z)
• Manipulating Feature Visualizations with Gradient Slingshots [54.31109240020007]
  We introduce a novel method for manipulating Feature Visualization (FV) without significantly impacting the model's decision-making process. We evaluate the effectiveness of our method on several neural network models and demonstrate its capabilities to hide the functionality of arbitrarily chosen neurons.
  arXiv  Detail & Related papers  (2024-01-11T18:57:17Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation [9.127485315153312]
  An interactive learning system is designed to create digit patterns and recognize them in real time. An evaluation with multiple datasets is conducted to determine its usability for active learning.
  arXiv  Detail & Related papers  (2023-10-02T19:21:24Z)
• ASF-Net: Robust Video Deraining via Temporal Alignment and Online Adaptive Learning [47.10392889695035]
  We propose a new computational paradigm, Alignment-Shift-Fusion Network (ASF-Net), which incorporates a temporal shift module. We construct a LArge-scale RAiny video dataset (LARA) which supports the development of this community. Our proposed approach exhibits superior performance in three benchmarks and compelling visual quality in real-world scenarios.
  arXiv  Detail & Related papers  (2023-09-02T14:50:13Z)
• STIP: A SpatioTemporal Information-Preserving and Perception-Augmented Model for High-Resolution Video Prediction [78.129039340528]
  We propose a Stemporal Information-Preserving and Perception-Augmented Model (STIP) to solve the above two problems. The proposed model aims to preserve thetemporal information for videos during the feature extraction and the state transitions. Experimental results show that the proposed STIP can predict videos with more satisfactory visual quality compared with a variety of state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-09T09:49:04Z)
• Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks [11.44782606621054]
  Convolutional neural networks (CNNs) have been widely used to perform intelligent visual object recognition. CNNs still suffer from severe accuracy degradation, particularly on illumination-variant datasets. We propose a new CNN method based on orientation fusion for visual object recognition.
  arXiv  Detail & Related papers  (2021-06-19T07:15:15Z)
• A Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDP [0.0]
  It is important to study the behavior of SNNs trained with unsupervised learning methods on video classification tasks. This paper presents methods of transposing temporal information into a static format, and then transforming the visual information into spikes using latency coding. We show the effect of the similarity in the shape and speed of certain actions on action recognition with spiking neural networks.
  arXiv  Detail & Related papers  (2021-05-31T07:07:48Z)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• Conditional physics informed neural networks [85.48030573849712]
  We introduce conditional PINNs (physics informed neural networks) for estimating the solution of classes of eigenvalue problems. We show that a single deep neural network can learn the solution of partial differential equations for an entire class of problems.
  arXiv  Detail & Related papers  (2021-04-06T18:29:14Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification [63.716247731036745]
  echo state networks have attracted many stares through time, mainly due to the simplicity and computational efficiency of their learning algorithm. This work addresses this issue by conducting an explainability study of Echo State Networks when applied to learning tasks with time series, image and video data. Specifically, the study proposes three different techniques capable of eliciting understandable information about the knowledge grasped by these recurrent models.
  arXiv  Detail & Related papers  (2021-02-17T08:56:33Z)
• Convolutional Neural Networks for Multispectral Image Cloud Masking [7.812073412066698]
  Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks. We study the use of different CNN architectures for cloud masking of Proba-V multispectral images.
  arXiv  Detail & Related papers  (2020-12-09T21:33:20Z)
• Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [126.51241919472356]
  We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
  arXiv  Detail & Related papers  (2020-11-26T14:43:04Z)
• A Prospective Study on Sequence-Driven Temporal Sampling and Ego-Motion Compensation for Action Recognition in the EPIC-Kitchens Dataset [68.8204255655161]
  Action recognition is one of the top-challenging research fields in computer vision. ego-motion recorded sequences have become of important relevance. The proposed method aims to cope with it by estimating this ego-motion or camera motion.
  arXiv  Detail & Related papers  (2020-08-26T14:44:45Z)
• Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey [77.99182201815763]
  Deep Neural Networks (DNNs) achieve state-of-the-art results in many different problem settings. DNNs are often treated as black box systems, which complicates their evaluation and validation. One promising field, inspired by the success of convolutional neural networks (CNNs) in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations.
  arXiv  Detail & Related papers  (2020-06-30T14:56:05Z)
• Bayesian Neural Networks: An Introduction and Survey [22.018605089162204]
  This article introduces Bayesian Neural Networks (BNNs) and the seminal research regarding their implementation. Different approximate inference methods are compared, and used to highlight where future research can improve on current methods.
  arXiv  Detail & Related papers  (2020-06-22T06:30:15Z)
• An Adversarial Approach for Explaining the Predictions of Deep Neural Networks [9.645196221785694]
  We present a novel algorithm for explaining the predictions of a deep neural network (DNN) using adversarial machine learning. Our approach identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN. Our analysis enables us to produce consistent and efficient explanations.
  arXiv  Detail & Related papers  (2020-05-20T18:06:53Z)
• Hold me tight! Influence of discriminative features on deep network boundaries [63.627760598441796]
  We propose a new perspective that relates dataset features to the distance of samples to the decision boundary. This enables us to carefully tweak the position of the training samples and measure the induced changes on the boundaries of CNNs trained on large-scale vision datasets.
  arXiv  Detail & Related papers  (2020-02-15T09:29:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.