Explainable AI (XAI) is an active research area to interpret a neural network's decision by ensuring transparency and trust in the task-specified learned models. Recently, perturbation-based model analysis has shown better interpretation, but …
Visualizing the features captured by Convolutional Neural Networks (CNNs) is one of the conventional approaches to interpret the predictions made by these models in numerous image recognition applications. Grad-CAM is a popular solution that provides …
TL;DR * We propose a state-of-the-art post-hoc CNN specific Visual XAI algorithm - SISE. * Input : A test image; The trained model * Output : A visual 2D heatmap * Properties : Noise-free, High resolution, Class discriminative and Correlates to model's prediction.
As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class …
Vision sensor data (RGB and Depth) collected from a semi-humanoid robot ‘Pepper’ provided by IATSL laboratory, are used to perform 3D human detection and tracking within a household setup enabling better assistance to old or sick-adults in home-care.
This project implements feature point detection and its matching between stereo pair images from KITTI dataset. For a given input RGB image from left camera, the features which are described to be an image region that is salient, local, repeatable, compact and efficient, are identified and studied by visual inspection for unreliability on matching.
Breast cancer classification on Keras (based on the implementaion of CancerNet algorithm by Adrian Rosebrock ).
Breast cancer is the most common form of cancer in women, and Invasive Ductal Carcinoma (IDC) is the most common form of breast cancer.
Worked on a 6 DOF non-linear optimization problem to determine the pose vector relating an Inertial Measurement Unit (IMU) to a LiDAR sensor based on the data collected on Zeus self-driving car during its operation.
This assignment focuses on 2D object detection and instance segmentation using the depth data. Motivated by the KITTI vision challenge for object detection and tracking, disparity map between the corresponding images from left (p2) and right (p3) camera are provided.
NEU_XAI Developed and studied XAI algorithms that generates saliency maps according to the importance of each corresponding pixels of the input test image towards the Machine Learning model’s predictive accuracy, with the aim of decoding complex black-box models.