There are multiple libraries that help us explain models of structured data like SHAP and LIME. This chapter explains computer vision model outputs.Kulkarni, AkshayShivananda, AdarshaSharma, Nitin Ranjan
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more. machine-learningcomputer-visiondeep-learninggrad-campytorchimage-classificationobject-detectionvisualizationsinterpretabilityclass-activation-mapsinterpretable...
When AI processes were simple and deterministic, trust in those processes was never an issue. Now that those processes have become more complex and less transparent, as in the example of CI above, trust has become essential for businesses that want to invest in AI. In his still-relevant deca...
He primarily focuses on areas of Data Analytics for the application involving Time-Series data. Jayanth has around 8 years of research and industrial experience, where he was working developing AI/ML/DL solutions for various application areas, such as retail optimization, computer ...
Label propagation for deep semi-supervised learning IEEE Conference on Computer Vision and Pattern Recognition (2019), pp. 5070-5079 Google Scholar [59] P. Sellars, A. Aviles-Rivero, C.B. Schönlieb Two cycle learning: clustering based regularisation for deep semi-supervised classification arXiv...
Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions The Industrial Revolution has shifted towards Industry 5.0, reinventing the Industry 4.0 operational process by introducing human elements into critical de... C Trivedi,P Bhattacharya,VK Prasad,... - 《IEEE Open Journal ...
Advanced ensemble machine-learning and explainable ai with hybridized clustering for solar irradiation prediction in Bangladesh The solar revolution in Bangladesh stands as a symbol of hope and self-reliance, illuminating communities and steering the nation towards a more sustainabl... MS Sevas,N ...
AI is a generic concept and an umbrella term that implies the use of a machine with limited human interference to model intelligent actions. It covers a broad range of research studies from machine intelligence for computer vision, robotics, natural language processing to more theoretical machine le...
deep-learning models are “opaque” to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by 作者: taking both a theoretical and a practical perspective,making the reader quickly capable of working with tools and code for Explainable AI. ...
a high-throughput tool for systematic antibody characterization and prediction of function is lacking. Here, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI), for preprocessing, feature engineering, and explainable, predictive machine learning on ...