Interpretability of deep neural networks: A review of methods, classification and hardware
Artificial intelligence, and especially deep neural networks, have evolved substantially in the
recent years, infiltrating numerous domains of applications, often greatly impactful to …
recent years, infiltrating numerous domains of applications, often greatly impactful to …
Emerging synergies in causality and deep generative models: A survey
In the field of artificial intelligence (AI), the quest to understand and model data-generating
processes (DGPs) is of paramount importance. Deep generative models (DGMs) have …
processes (DGPs) is of paramount importance. Deep generative models (DGMs) have …
Diffusion models for counterfactual explanations
Counterfactual explanations have shown promising results as a post-hoc framework to make
image classifiers more explainable. In this paper, we propose DiME, a method allowing the …
image classifiers more explainable. In this paper, we propose DiME, a method allowing the …
Adversarial counterfactual visual explanations
Counterfactual explanations and adversarial attacks have a related goal: flipping output
labels with minimal perturbations regardless of their characteristics. Yet, adversarial attacks …
labels with minimal perturbations regardless of their characteristics. Yet, adversarial attacks …
FS-SCF network: Neural network interpretability based on counterfactual generation and feature selection for fault diagnosis
Interpretability of neural networks aims at the development of models that can give
information to the end-user about its inner workings and/or predictions, while keeping the …
information to the end-user about its inner workings and/or predictions, while keeping the …
Ganterfactual—counterfactual explanations for medical non-experts using generative adversarial learning
With the ongoing rise of machine learning, the need for methods for explaining decisions
made by artificial intelligence systems is becoming a more and more important topic …
made by artificial intelligence systems is becoming a more and more important topic …
Designing counterfactual generators using deep model inversion
J Thiagarajan, VS Narayanaswamy… - Advances in …, 2021 - proceedings.neurips.cc
Explanation techniques that synthesize small, interpretable changes to a given image while
producing desired changes in the model prediction have become popular for introspecting …
producing desired changes in the model prediction have become popular for introspecting …
A generalized explanation framework for visualization of deep learning model predictions
P Wang, N Vasconcelos - IEEE Transactions on Pattern …, 2023 - ieeexplore.ieee.org
Attribution-based explanations are popular in computer vision but of limited use for fine-
grained classification problems typical of expert domains, where classes differ by subtle …
grained classification problems typical of expert domains, where classes differ by subtle …
VCNet: A self-explaining model for realistic counterfactual generation
Counterfactual explanation is a common class of methods to make local explanations of
machine learning decisions. For a given instance, these methods aim to find the smallest …
machine learning decisions. For a given instance, these methods aim to find the smallest …
Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization
O Rotem, T Schwartz, R Maor, Y Tauber… - Nature …, 2024 - nature.com
The success of deep learning in identifying complex patterns exceeding human intuition
comes at the cost of interpretability. Non-linear entanglement of image features makes deep …
comes at the cost of interpretability. Non-linear entanglement of image features makes deep …