MyBlog

Welcome to myblog to understanding of deep learning

Explainability Methods for Point Clouds II

December 30, 2024

As the most widely used 3D data format, point cloud recognition has become an area of increasing research focus. However, as deep learning models continue to improve their performance, the increasing complexity of model architectures raises concerns about their trustworthiness, especially for data such as point clouds that are often used in safety-critical applications like autonomous driving. To address this challenge, a previous blog post presented an explanation method based on a surrogate model for point clouds. In this article, we will now propose further research on the explainability of point clouds.

Naghmana Asif

More Stories

Generating passwords with deep learning

December 30, 2024

The most common way to identify users on websites and devices is through passwords. They have been used for decades for identification, are familiar to users and developers, and are easy to use. However, on their own, passwords are not particularly secure. Users tend to reuse the same password on multiple sites and use simple, short passwords consisting of common character strings and dictionary words. Thus, people often exhibit recognizable patterns when creating passwords. Studying these patterns and using the identified regularities to create new, more secure passwords is the focus of the research area known as password guessing.

Naghmana Asif

AI in radiology – Classifying and explaining hemorrhagic strokes with deep learning

December 30, 2024

“Time is brain” – this well-known phrase encapsulates the challenge of a stroke. When a person suffers a stroke, quick action is crucial to minimize lasting damage. The cause of a stroke can be either a vessel blockage (ischemic) or bleeding in the brain (hemorrhagic). While blood-thinning medications can restore blood flow in the case of a vessel blockage, they would be counterproductive in the event of bleeding. Therefore, imaging diagnostics are essential to quickly differentiate between these two conditions and, in the case of a hemorrhagic stroke, examine the location and size of the bleeding. Typically, a Computed Tomography (CT) (see Figure 1) scan of the skull is performed, consisting of multiple individual images (layers) that collectively create a three-dimensional impression of the head.

Naghmana Asif

An Approach to Granular Testing of Deep Neural Networks

December 30, 2024

Deep Neural Networks (DNNs) have shown phenomenal performance, reaching or surpassing human expert-level performance in various domains. Computer vision, which is essential for the success of technologies like autonomous driving or automated medical diagnosis, uses a version of DNNs called Convolutional Neural Networks (CNNs). CNNs have replaced classical Machine Learning modules for complex tasks like Semantic Segmentation or Object Detection. Like other DNNs, CNNs are also black boxes where the underlying function aiming to approximate the real-world distribution of data is learned using training data, and the decision-making process of the DNN itself is not easily interpretable by humans.

Naghmana Asif

Navigating the Evolution of Automatic Speech Recognition (ASR)

December 30, 2024

Automatic Speech Recognition (ASR) is a rapidly advancing technology that holds immense relevance in today’s digital age. As a voice-based technology it is a beneficial way to make Human Computer Interaction more accessible. However, the inner workings of ASR can be complex, so we aim to demystify the concept of ASR and present it in a simple and understandable manner while giving an overview of the different time periods and what led to the transitions between them.

Naghmana Asif