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Showing result 1 - 5 of 14 essays matching the above criteria.

  1. 1. Towards gradient faithfulness and beyond

    University essay from Högskolan i Halmstad/Akademin för informationsteknologi

    Author : Vincenzo Buono; Isak Åkesson; [2023]
    Keywords : XAI; Visual Explanations; CAM; Grad-CAM; Expected Grad-CAM; Hyper Expected Grad; Class Activation Maps; Explainable AI; Faithfulness; Neural Network interpretability; Hyper Resolution CAM; Super Resolution CAM; Natural Encoding;

    Abstract : The riveting interplay of industrialization, informalization, and exponential technological growth of recent years has shifted the attention from classical machine learning techniques to more sophisticated deep learning approaches; yet its intrinsic black-box nature has been impeding its widespread adoption in transparency-critical operations. In this rapidly evolving landscape, where the symbiotic relationship between research and practical applications has never been more interwoven, the contribution of this paper is twofold: advancing gradient faithfulness of CAM methods and exploring new frontiers beyond it. READ MORE

  3. 2. Classifying Motion Patterns of Bikes using Machine Learning

    University essay from Lunds universitet/Institutionen för reglerteknik

    Author : Filip Larsson; Pontus Hallqvist; [2023]
    Keywords : Technology and Engineering;

    Abstract : Electric bikes have become ubiquitous in traffic, and with a growing user base and expensive prices, a demand for bike protection is increasing. Bike protection applications could include detecting and notifying the owner if their bike has been stolen or fallen over. READ MORE

  4. 3. Is eXplainable AI suitable as a hypotheses generating tool for medical research? Comparing basic pathology annotation with heat maps to find out

    University essay from Uppsala universitet/Statistiska institutionen

    Author : Albert Adlersson; [2023]
    Keywords : black box; eXplainable AI XAI ; Convolutional Neural Network CNN ; Mi- crosatellite Instability MSI ; colon cancer; gastric cancer; hypotheses generating; hypotheses generating tool; medical research;

    Abstract : Hypothesis testing has long been a formal and standardized process. Hypothesis generation, on the other hand, remains largely informal. This thesis assess whether eXplainable AI (XAI) can aid in the standardization of hypothesis generation through its utilization as a hypothesis generating tool for medical research. READ MORE

  5. 4. Design, implementation and evaluation of a deep learning prototype to classify non-pigmented malignant skin cancer from dermatoscopic images

    University essay from Lunds universitet/Matematik LTH

    Author : Maria del Pilar Aguilera Manzanera; [2022]
    Keywords : Melanoma; Skin cancer; Dermatoscopy; Image classification; Machine learning; Artificial intelligence; Convolutional neural networks; Dermatology; Squamous cell carcinoma; Basal cell carcinoma; Actinic keratosis; Computer-aided Diagnostics; Digital dermatology; Technology and Engineering;

    Abstract : The current trends for most fair-skinned populations are that the incidence of melanoma and non-pigmented skin lesions is growing, and this growing trend will continue for the upcoming years. The emergence of deep learning networks and their promising results in solving real-world healthcare problems and improving diagnostic accuracy opens new possibilities. READ MORE

  6. 5. Improving Visual Question Answering by Leveraging Depth and Adapting Explainability

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Amrita Kaur Panesar; [2022]
    Keywords : VQA; RGB-D; Explainability; Grad-CAM; Human-Robot Interaction; VQA; RGB-D; Förklarbarhet; Grad-CAM; Samspel människa-robot;

    Abstract : To produce smooth human-robot interactions, it is important for robots to be able to answer users’ questions accurately and provide a suitable explanation for why they arrive to the answer they provide. However, in the wild, the user may ask the robot questions relating to aspects of the scene that the robot is unfamiliar with and hence be unable to answer correctly all of the time. READ MORE