Demystifying CNNs for Images by Matched Filters

• URL: http://arxiv.org/abs/2210.08521v1
• Date: Sun, 16 Oct 2022 12:39:17 GMT
• Title: Demystifying CNNs for Images by Matched Filters
• Authors: Shengxi Li, Xinyi Zhao, Ljubisa Stankovic, Danilo Mandic
• Abstract summary: convolution neural networks (CNN) have been revolutionising the way we approach and use intelligent machines in the Big Data era. CNNs have been put under scrutiny owing to their textitblack-box nature, as well as the lack of theoretical support and physical meanings of their operation. This paper attempts to demystify the operation of CNNs by employing the perspective of matched filtering.
• Score: 13.121514086503591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of convolution neural networks (CNN) has been revolutionising the way we approach and use intelligent machines in the Big Data era. Despite success, CNNs have been consistently put under scrutiny owing to their \textit{black-box} nature, an \textit{ad hoc} manner of their construction, together with the lack of theoretical support and physical meanings of their operation. This has been prohibitive to both the quantitative and qualitative understanding of CNNs, and their application in more sensitive areas such as AI for health. We set out to address these issues, and in this way demystify the operation of CNNs, by employing the perspective of matched filtering. We first illuminate that the convolution operation, the very core of CNNs, represents a matched filter which aims to identify the presence of features in input data. This then serves as a vehicle to interpret the convolution-activation-pooling chain in CNNs under the theoretical umbrella of matched filtering, a common operation in signal processing. We further provide extensive examples and experiments to illustrate this connection, whereby the learning in CNNs is shown to also perform matched filtering, which further sheds light onto physical meaning of learnt parameters and layers. It is our hope that this material will provide new insights into the understanding, constructing and analysing of CNNs, as well as paving the way for developing new methods and architectures of CNNs.

Related papers

• A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks [2.249916681499244]
  We introduce a neurosymbolic framework called NeSyBiCor for bias correction in a trained CNN. We show that our framework successfully corrects the biases of CNNs trained with subsets of classes from the "Places" dataset.
  arXiv  Detail & Related papers  (2024-05-24T19:09:53Z)
• Understanding and Improving CNNs with Complex Structure Tensor: A Biometrics Study [47.03015281370405]
  We show that the use of Complex Structure, which contains compact orientation features with certainties, improves identification accuracy compared to using grayscale inputs alone. This suggests that the upfront use of orientation features in CNNs, a strategy seen in mammalian vision, not only mitigates their limitations but also enhances their explainability and relevance to thin-clients.
  arXiv  Detail & Related papers  (2024-04-24T02:51:13Z)
• PICNN: A Pathway towards Interpretable Convolutional Neural Networks [12.31424771480963]
  We introduce a novel pathway to alleviate the entanglement between filters and image classes. We use the Bernoulli sampling to generate the filter-cluster assignment matrix from a learnable filter-class correspondence matrix. We evaluate the effectiveness of our method on ten widely used network architectures.
  arXiv  Detail & Related papers  (2023-12-19T11:36:03Z)
• Transferability of Convolutional Neural Networks in Stationary Learning Tasks [96.00428692404354]
  We introduce a novel framework for efficient training of convolutional neural networks (CNNs) for large-scale spatial problems. We show that a CNN trained on small windows of such signals achieves a nearly performance on much larger windows without retraining. Our results show that the CNN is able to tackle problems with many hundreds of agents after being trained with fewer than ten.
  arXiv  Detail & Related papers  (2023-07-21T13:51:45Z)
• A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations [0.0]
  Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Yet it remains poorly understood how CNNs actually make their decisions, what the nature of their internal representations is, and how their recognition strategies differ from humans.
  arXiv  Detail & Related papers  (2022-12-12T16:40:29Z)
• Deeply Explain CNN via Hierarchical Decomposition [75.01251659472584]
  In computer vision, some attribution methods for explaining CNNs attempt to study how the intermediate features affect the network prediction. This paper introduces a hierarchical decomposition framework to explain CNN's decision-making process in a top-down manner.
  arXiv  Detail & Related papers  (2022-01-23T07:56:04Z)
• Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial [7.826806223782053]
  Convolutional Neural Networks (CNN) are a de-facto standard for the analysis of large volumes of signals and images. We revisit their operation from first principles and a matched filtering perspective. It is our hope that this tutorial will help shed new light and physical intuition into the understanding and further development of deep neural networks.
  arXiv  Detail & Related papers  (2021-08-26T09:07:49Z)
• BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks [65.2021953284622]
  We study robustness of CNNs against white-box and black-box adversarial attacks. Results are shown for distilled CNNs, agent-based state-of-the-art pruned models, and binarized neural networks.
  arXiv  Detail & Related papers  (2021-03-14T20:43:19Z)
• The Mind's Eye: Visualizing Class-Agnostic Features of CNNs [92.39082696657874]
  We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer. Our method uses a dual-objective activation and distance loss, without requiring a generator network nor modifications to the original model.
  arXiv  Detail & Related papers  (2021-01-29T07:46:39Z)
• Curriculum By Smoothing [52.08553521577014]
  Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification, detection, and segmentation. We propose an elegant curriculum based scheme that smoothes the feature embedding of a CNN using anti-aliasing or low-pass filters. As the amount of information in the feature maps increases during training, the network is able to progressively learn better representations of the data.
  arXiv  Detail & Related papers  (2020-03-03T07:27:44Z)
• Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification [3.0204693431381515]
  We adjust the architecture of the tiled convolutional neural network (tiled CNN) to improve its extraction of salient features for sentiment analysis. Knowing that the major drawback of the tiled CNN in the NLP field is its inflexible filter structure, we propose a novel architecture called hybrid tiled CNN. Experiments on the datasets of IMDB movie reviews and SemEval 2017 demonstrate the efficiency of the hybrid tiled CNN.
  arXiv  Detail & Related papers  (2020-01-31T14:08:15Z)

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.