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Found 10 papers, 4 papers with code
Significantly Improving Zero-Shot X-ray Pathology Classification via Fine-tuning Pre-trained Image-Text Encoders
However, large-scale and high-quality data to train powerful neural networks are rare in the medical domain as the labeling must be done by qualified experts.
TransCAM: Transformer Attention-based CAM Refinement for Weakly Supervised Semantic Segmentation
In this paper, we propose TransCAM, a Conformer-based solution to WSSS that explicitly leverages the attention weights from the transformer branch of the Conformer to refine the CAM generated from the CNN branch.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification
Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification.
Look at here : Utilizing supervision to attend subtle key regions
In addition, using the class activation map, we identified that the Cut\&Remain methods drive a model to focus on relevant subtle and small regions efficiently.
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness
Recent years have seen the introduction of a range of methods for post-hoc explainability of image classifier predictions.
Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring
However, the average gradient-based terms deployed in this method underestimates the contribution of the representations discovered by the model to its predictions.
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks
Explainable AI (XAI) is an active research area to interpret a neural network's decision by ensuring transparency and trust in the task-specified learned models.
Computational Efficiency
Explainable Artificial Intelligence (XAI)
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation
In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation.
Online Class-Incremental Continual Learning with Adversarial Shapley Value
As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption.
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation
Deep Neural Network based classifiers are known to be vulnerable to perturbations of inputs constructed by an adversarial attack to force misclassification.
Adversarial Attack
Explainable Artificial Intelligence (XAI)
