Explainable AI

Counterfactual Explanation Based on Gradual Construction for Deep Networks

1. Counterfactual Explanation Counterfactual Explanation: Given input data that are classified as a class from a deep network, it is to perturb the subset of features in the input data such that the model is forced to predict the perturbed data as a target class. The Framework for counterfactual explanation is described in Fig 1. From perturbed data, we can interpret that the pre-trained model t..

A Disentangling Invertible Interpretation Network for Explaining Latent Representations

1. Interpreting hidden representations 1.1 Invertible transformation of hidden representations Input image: \( x \in \mathbb{R}^{H \times W \times 3 } \) Sub-network of \(f \) including hidden layers: \(E\) Latent (original) representation: \( z=\mathbb{E} (x) \in \mathbb{R}^{H \times W \times 3 = N}\) Sub-network after the hidden layer: \( G \) \( f(x)=G \cdot E(x) \) In A Disentangling Inverti..

Interpretable And Fine-grained Visual Explanations For Convolutional Neural Networks

1. Goal In Interpretable And Fine-grained Visual Explanations For Convolutional Neural Networks, authors propose an optimization-based visual explanation method, which highlights the evidence in the input images for a specific prediction. 1.1 Sub-goal [A]: Defending against adversarial evidence (i.e. faulty evidence due to artifacts). [B]: Providing explanations which are both fine-grained and p..

GradCAM

1. What is the goal of GradCAM?? The goal of GradCAM is to produce a coarse localization map highlighting the important regions in the image for predicting the concept (class). GradCAM uses the gradients of any target concept (such as "cat") flowing into the final convolutional layer. Note: I (da2so) will only deal with the problem of image classification in the following contents. The property ..