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Found 5 papers, 2 papers with code
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.
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.
