CAM-Based Methods Can See through Walls

• URL: http://arxiv.org/abs/2404.01964v2
• Date: Mon, 8 Jul 2024 11:00:51 GMT
• Title: CAM-Based Methods Can See through Walls
• Authors: Magamed Taimeskhanov, Ronan Sicre, Damien Garreau,
• Abstract summary: We show that most CAM-based interpretability methods can incorrectly attribute an important score to parts of the image that the model cannot see. We train a VGG-like model constrained to not use the lower part of the image and observe positive scores in the unseen part of the image. This behavior is evaluated quantitatively on two new datasets.
• Score: 6.356330972370584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: CAM-based methods are widely-used post-hoc interpretability method that produce a saliency map to explain the decision of an image classification model. The saliency map highlights the important areas of the image relevant to the prediction. In this paper, we show that most of these methods can incorrectly attribute an important score to parts of the image that the model cannot see. We show that this phenomenon occurs both theoretically and experimentally. On the theory side, we analyze the behavior of GradCAM on a simple masked CNN model at initialization. Experimentally, we train a VGG-like model constrained to not use the lower part of the image and nevertheless observe positive scores in the unseen part of the image. This behavior is evaluated quantitatively on two new datasets. We believe that this is problematic, potentially leading to mis-interpretation of the model's behavior.

Related papers

• Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models [55.2480439325792]
  We propose a new pretext task, which is to perform simultaneously image denoising and mask prediction on the first domain. We show that fine-tuning a model pretrained using this approach leads to better results than fine-tuning a similar model trained using either supervised or unsupervised pretraining.
  arXiv  Detail & Related papers  (2024-08-06T20:19:06Z)
• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• COSE: A Consistency-Sensitivity Metric for Saliency on Image Classification [21.3855970055692]
  We present a set of metrics that utilize vision priors to assess the performance of saliency methods on image classification tasks. We show that although saliency methods are thought to be architecture-independent, most methods could better explain transformer-based models over convolutional-based models.
  arXiv  Detail & Related papers  (2023-09-20T01:06:44Z)
• 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)
• Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification [17.373054348176932]
  In this work, a post-hoc interpretation tool named feature activation map (FAM) is proposed. FAM can interpret deep learning models without FC layers as a classifier. Experiments conducted on ten deep learning models for few-shot image classification, contrastive learning image classification and image retrieval tasks demonstrate the effectiveness of the proposed FAM algorithm.
  arXiv  Detail & Related papers  (2023-07-11T05:33:46Z)
• Sanity checks and improvements for patch visualisation in prototype-based image classification [0.0]
  We perform an in-depth analysis of the visualisation methods implemented in two popular self-explaining models for visual classification based on prototypes. We first show that such methods do not correctly identify the regions of interest inside of the images, and therefore do not reflect the model behaviour. We discuss the implications of our findings for other prototype-based models sharing the same visualisation method.
  arXiv  Detail & Related papers  (2023-01-20T15:13:04Z)
• Unsupervised Deep Learning Meets Chan-Vese Model [77.24463525356566]
  We propose an unsupervised image segmentation approach that integrates the Chan-Vese (CV) model with deep neural networks. Our basic idea is to apply a deep neural network that maps the image into a latent space to alleviate the violation of the piecewise constant assumption in image space.
  arXiv  Detail & Related papers  (2022-04-14T13:23:57Z)
• Semantic keypoint-based pose estimation from single RGB frames [64.80395521735463]
  We present an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. We show that our approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios.
  arXiv  Detail & Related papers  (2022-04-12T15:03:51Z)
• Generalizing Adversarial Explanations with Grad-CAM [7.165984630575092]
  We present a novel method that extends Grad-CAM from example-based explanations to a method for explaining global model behaviour. For our experiment, we study adversarial attacks on deep models such as VGG16, ResNet50, and ResNet101, and wide models such as InceptionNetv3 and XceptionNet. The proposed method can be used to understand adversarial attacks and explain the behaviour of black box CNN models for image analysis.
  arXiv  Detail & Related papers  (2022-04-11T22:09:21Z)
• CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency [61.40511574314069]
  Backpropagation image saliency aims at explaining model predictions by estimating model-centric importance of individual pixels in the input. We propose CAMERAS, a technique to compute high-fidelity backpropagation saliency maps without requiring any external priors.
  arXiv  Detail & Related papers  (2021-06-20T08:20:56Z)
• Towards Visually Explaining Similarity Models [29.704524987493766]
  We present a method to generate gradient-based visual attention for image similarity predictors. By relying solely on the learned feature embedding, we show that our approach can be applied to any kind of CNN-based similarity architecture. We show that our resulting attention maps serve more than just interpretability; they can be infused into the model learning process itself with new trainable constraints.
  arXiv  Detail & Related papers  (2020-08-13T17:47:41Z)

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.