Abstract: There is provided a method and a system for customized image denoising with interpretability. A deep neural network (NN) is trained to denoise an image on a training dataset including pairs of noisy and corresponding clean images acquired from an imaging apparatus, where during the training a structured covariance score (SCS) indicative of a performance of the deep NN in recovering content of corresponding clean images relative to the denoised image is determined based on sparse conditional correlations. A test noisy image is received and denoised by the deep NN. A user feedback score indicative of user satisfaction of the denoising is obtained. A quality parameter is obtained based on the SCS and a quality metric indicative of denoised image quality is obtained from a pretrained NN, and compared with the user feedback score. If the SCS is above the user feedback score, the deep NN is provided for denoising.

Abstract: A method and a system for generating an augmented scene graph of an image and for training an explainable knowledge based (KB) visual question answering (VQA) machine learning (ML) model are provided. A scene graph encoding spatial and semantic features of objects and relations between objects in the image is obtained. An augmented scene graph is generated by embedding a knowledge graph to enhance the scene graph. An embedded set of questions and associated answers related to the image are obtained. The KB VQA ML model is trained to provide an answer to a given question related to the image based on the augmented scene graph and the embedded set of questions and associated answers. The KB VQA ML model is trained to retrieve a subgraph linking the question and the associated answer as a potential explanation for the answer.

Abstract: There is provided a method and a system for ranking subgraphs as potential explanations for a labelled edge type class. A first graph representing labelled digital items is obtained, where each labelled digital item is represented as an entity node connected via a labelled edge type to a property value node. The first graph is combined with a second graph representing structured relations in the labelled digital items to obtain a combined graph. Unlabelled digital items are received and matched to respective subgraphs in the combined graph. A machine learning model is used to embed the combined graph to generate graph vectors, and an expressivity score between matched subgraphs and respective labelled edge types based on the generated graph vectors. The matched subgraphs are ranked based on the expressivity score to obtain a ranked set of subgraphs as potential explanations for a respective labelled edge type class.