FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs

• URL: http://arxiv.org/abs/2206.08792v1
• Date: Fri, 17 Jun 2022 14:08:39 GMT
• Title: FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs
• Authors: Hui Li, Zihao Li, Rui Ma, Tieru Wu
• Abstract summary: Class activation map (CAM) has been widely studied for visual explanation of the internal working mechanism of convolutional neural networks. We propose a novel CAM weighting scheme, named FD-CAM, to improve both the faithfulness and discriminability of the CNN visual explanation.
• Score: 7.956110316017118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Class activation map (CAM) has been widely studied for visual explanation of the internal working mechanism of convolutional neural networks. The key of existing CAM-based methods is to compute effective weights to combine activation maps in the target convolution layer. Existing gradient and score based weighting schemes have shown superiority in ensuring either the discriminability or faithfulness of the CAM, but they normally cannot excel in both properties. In this paper, we propose a novel CAM weighting scheme, named FD-CAM, to improve both the faithfulness and discriminability of the CAM-based CNN visual explanation. First, we improve the faithfulness and discriminability of the score-based weights by performing a grouped channel switching operation. Specifically, for each channel, we compute its similarity group and switch the group of channels on or off simultaneously to compute changes in the class prediction score as the weights. Then, we combine the improved score-based weights with the conventional gradient-based weights so that the discriminability of the final CAM can be further improved. We perform extensive comparisons with the state-of-the-art CAM algorithms. The quantitative and qualitative results show our FD-CAM can produce more faithful and more discriminative visual explanations of the CNNs. We also conduct experiments to verify the effectiveness of the proposed grouped channel switching and weight combination scheme on improving the results. Our code is available at https://github.com/crishhh1998/FD-CAM.

Related papers

• Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation [4.818865062632567]
  Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays. One way of interpreting a CNN is the use of class activation maps (CAMs) that represent heatmaps. We propose a transfer between existing classification- and segmentation-based methods for more detailed, explainable, and consistent results.
  arXiv  Detail & Related papers  (2024-09-30T13:43:00Z)
• BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications [69.22739434619531]
  We propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. By evaluating BroadCAM on VOC2012 and BCSS-WSSS for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance.
  arXiv  Detail & Related papers  (2023-09-07T06:45:43Z)
• An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification [55.28171619580959]
  This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting.
  arXiv  Detail & Related papers  (2023-07-25T11:51:14Z)
• Learning Partial Correlation based Deep Visual Representation for Image Classification [61.0532370259644]
  We formulate sparse inverse covariance estimation (SICE) as a novel structured layer of CNN. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem. Experiments show the efficacy and superior classification performance of our model.
  arXiv  Detail & Related papers  (2023-04-23T10:09:01Z)
• Cluster-CAM: Cluster-Weighted Visual Interpretation of CNNs' Decision in Image Classification [12.971559051829658]
  Cluster-CAM is an effective and efficient gradient-free CNN interpretation algorithm. We propose an artful strategy to forge a cognition-base map and cognition-scissors from clustered feature maps.
  arXiv  Detail & Related papers  (2023-02-03T10:38:20Z)
• Orthogonal SVD Covariance Conditioning and Latent Disentanglement [65.67315418971688]
  Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned. We propose Nearest Orthogonal Gradient (NOG) and Optimal Learning Rate (OLR) Experiments on visual recognition demonstrate that our methods can simultaneously improve covariance conditioning and generalization.
  arXiv  Detail & Related papers  (2022-12-11T20:31:31Z)
• Attention-based Class Activation Diffusion for Weakly-Supervised Semantic Segmentation [98.306533433627]
  extracting class activation maps (CAM) is a key step for weakly-supervised semantic segmentation (WSSS) This paper proposes a new method to couple CAM and Attention matrix in a probabilistic Diffusion way, and dub it AD-CAM. Experiments show that AD-CAM as pseudo labels can yield stronger WSSS models than the state-of-the-art variants of CAM.
  arXiv  Detail & Related papers  (2022-11-20T10:06:32Z)
• Towards Learning Spatially Discriminative Feature Representations [26.554140976236052]
  We propose a novel loss function, termed as CAM-loss, to constrain the embedded feature maps with the class activation maps (CAMs) CAM-loss drives the backbone to express the features of target category and suppress the features of non-target categories or background. Experimental results show that CAM-loss is applicable to a variety of network structures and can be combined with mainstream regularization methods to improve the performance of image classification.
  arXiv  Detail & Related papers  (2021-09-03T08:04:17Z)
• Keep CALM and Improve Visual Feature Attribution [42.784665606132]
  The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. We improve CAM by explicitly incorporating a latent variable encoding the location of the cue for recognition in the formulation. The resulting model, class activation latent mapping, or CALM, is trained with the expectation-maximization algorithm.
  arXiv  Detail & Related papers  (2021-06-15T03:33:25Z)
• Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks [89.56292219019163]
  Explanation methods facilitate the development of models that learn meaningful concepts and avoid exploiting spurious correlations. We illustrate a previously unrecognized limitation of the popular neural network explanation method Grad-CAM. We propose HiResCAM, a class-specific explanation method that is guaranteed to highlight only the locations the model used to make each prediction.
  arXiv  Detail & Related papers  (2020-11-17T19:26:14Z)
• Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs [29.731732363623713]
  Class Activation Mapping (CAM) methods have been proposed to discover the connection between CNN's decision and image regions. In this paper, we introduce two axioms -- Conservation and Sensitivity -- to the visualization paradigm of the CAM methods. A dedicated Axiom-based Grad-CAM (XGrad-CAM) is proposed to satisfy these axioms as much as possible.
  arXiv  Detail & Related papers  (2020-08-05T18:42:33Z)

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.