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Hermitry Ratio: Evaluating the validity of perturbation methods for explainable deep learning
Perturbation methods are model-agnostic methods used to generate heatmaps to explain black-box algorithms such as deep neural networks.
Explainable Artificial Intelligence (XAI)
Image Classification
The 3TConv: An Intrinsic Approach to Explainable 3D CNNs
In a 3TConv the 3D convolutional filter is obtained by learning a 2D filter and a set of temporal transformation parameters, resulting in a sparse filter requiring less parameters.
Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space
We introduce a new framework for hyperrealistic reconstruction of perceived naturalistic stimuli from brain recordings.
Explainable Deep Learning: A Field Guide for the Uninitiated
The field guide: i) Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning, ii) discusses the evaluations for model explanations, iii) places explainability in the context of other related deep learning research areas, and iv) finally elaborates on user-oriented explanation designing and potential future directions on explainable deep learning.
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are analyzed in the context of Deep Neural Networks (DNNs), a taxonomy for the classification of existing explanation methods is introduced, and finally, the various classes of explanation methods are analyzed to verify if user concerns are justified.
