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Found 11 papers, 4 papers with code
CA-Stream: Attention-based pooling for interpretable image recognition
Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map.
A Learning Paradigm for Interpretable Gradients
This paper studies interpretability of convolutional networks by means of saliency maps.
DP-Net: Learning Discriminative Parts for image recognition
This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module.
CAM-Based Methods Can See through Walls
CAM-based methods are widely-used post-hoc interpretability method that produce a saliency map to explain the decision of an image classification model.
Revisiting Transferable Adversarial Image Examples: Attack Categorization, Evaluation Guidelines, and New Insights
Transferable adversarial examples raise critical security concerns in real-world, black-box attack scenarios.
Opti-CAM: Optimizing saliency maps for interpretability
Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as saliency maps.
Towards Good Practices in Evaluating Transfer Adversarial Attacks
In this work, we design good practices to address these limitations, and we present the first comprehensive evaluation of transfer attacks, covering 23 representative attacks against 9 defenses on ImageNet.
Sparse matrix products for neural network compression
Over-parameterization of neural networks is a well known issue that comes along with their great performance.
Unsupervised part learning for visual recognition
This strategy opens the door to the use of PBM in new applications for which the notion of image categories is irrelevant, such as instance-based image retrieval, for example.
Automatic discovery of discriminative parts as a quadratic assignment problem
Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built.
Particular object retrieval with integral max-pooling of CNN activations
Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations.
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