Visual Explanations for Convolutional Neural Networks via Latent Traversal of Generative Adversarial Networks

• URL: http://arxiv.org/abs/2111.00116v2
• Date: Tue, 2 Nov 2021 00:42:41 GMT
• Title: Visual Explanations for Convolutional Neural Networks via Latent Traversal of Generative Adversarial Networks
• Authors: Amil Dravid, Aggelos K. Katsaggelos
• Abstract summary: We present a method for interpreting what a convolutional neural network (CNN) has learned by utilizing Generative Adversarial Networks (GANs) Our GAN framework disentangles lung structure from COVID-19 features. Using this GAN, we can visualize the transition of a pair of COVID negative lungs in a chest radiograph to a COVID positive pair by interpolating in the latent space of the GAN.
• Score: 17.475341881835355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lack of explainability in artificial intelligence, specifically deep neural networks, remains a bottleneck for implementing models in practice. Popular techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) provide a coarse map of salient features in an image, which rarely tells the whole story of what a convolutional neural network (CNN) learned. Using COVID-19 chest X-rays, we present a method for interpreting what a CNN has learned by utilizing Generative Adversarial Networks (GANs). Our GAN framework disentangles lung structure from COVID-19 features. Using this GAN, we can visualize the transition of a pair of COVID negative lungs in a chest radiograph to a COVID positive pair by interpolating in the latent space of the GAN, which provides fine-grained visualization of how the CNN responds to varying features within the lungs.

Related papers

• U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation [48.40120035775506]
  Kolmogorov-Arnold Networks (KANs) reshape the neural network learning via the stack of non-linear learnable activation functions. We investigate, modify and re-design the established U-Net pipeline by integrating the dedicated KAN layers on the tokenized intermediate representation, termed U-KAN. We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures.
  arXiv  Detail & Related papers  (2024-06-05T04:13:03Z)
• CNN2GNN: How to Bridge CNN with GNN [59.42117676779735]
  We propose a novel CNN2GNN framework to unify CNN and GNN together via distillation. The performance of distilled boosted'' two-layer GNN on Mini-ImageNet is much higher than CNN containing dozens of layers such as ResNet152.
  arXiv  Detail & Related papers  (2024-04-23T08:19:08Z)
• Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective [53.999128831324576]
  Graph neural networks (GNNs) have pioneered advancements in graph representation learning. This study investigates the role of graph convolution within the context of feature learning theory.
  arXiv  Detail & Related papers  (2023-06-24T10:21:11Z)
• Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition [15.93566875893684]
  We propose a pure CNN architecture named Topology-aware CNN (Ta-CNN) in this paper. We develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations. In particular, we develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations.
  arXiv  Detail & Related papers  (2021-12-08T09:02:50Z)
• Automated airway segmentation by learning graphical structure [0.76146285961466]
  We put forward an advanced method for airway segmentation based on the existent convolutional neural network (CNN) and graph neural network (GNN) The proposed model shows that compared with the CNN-only method, the combination of CNN and GNN has a better performance in that the bronchi in the chest CT scans can be detected in most cases.
  arXiv  Detail & Related papers  (2021-09-30T01:37:31Z)
• 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)
• 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)
• 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 Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays [2.2843885788439797]
  We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal. We were able to reach test accuracy of 100% on our test dataset.
  arXiv  Detail & Related papers  (2020-04-30T13:20:42Z)
• 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)

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.