Essays about: "deep generative model"
Showing result 1 - 5 of 95 essays containing the words deep generative model.
-
1. Learning a Grasp Prediction Model for Forestry Applications
University essay from Umeå universitet/Institutionen för fysikAbstract : Since the advent of machine learning and machine vision methods, progress has been made in tackling the long-standing research question of autonomous grasping of arbitrary objects using robotic end-effectors. Building on these efforts, we focus on a subset of the general grasping problem concerning the automation of a forwarder. READ MORE
-
2. Transforming Chess: Investigating Decoder-Only Architecture for Generating Realistic Game-Like Positions
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : Chess is a deep and intricate game, the master of which depends on learning tens of thousands of the patterns that may occur on the board. At Noctie, their mission is to aid this learning process through humanlike chess AI. A prominent challenge lies in curating instructive chess positions for students. READ MORE
-
3. Virtual H&E Staining Using PLS Microscopy and Neural Networks
University essay from Lunds universitet/Matematik LTHAbstract : Histopathological examination, crucial in diagnosing diseases such as cancer, traditionally relies on time- and resource-consuming, poorly standardized chemical staining for tissue visualization. This thesis presents a novel digital alternative using generative neural networks and a point light source (PLS) microscope to transform unstained skin tissue images into their stained counterparts. READ MORE
-
4. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance
University essay from Uppsala universitet/Avdelningen för beräkningsvetenskapAbstract : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. READ MORE
-
5. Domain Adaptation for Multi-Contrast Image Segmentation in Cardiac Magnetic Resonance Imaging
University essay from KTH/Skolan för kemi, bioteknologi och hälsa (CBH)Abstract : Accurate segmentation of the ventricles and myocardium on Cardiac Magnetic Resonance (CMR) images is crucial to assess the functioning of the heart or to diagnose patients suffering from myocardial infarction. However, the domain shift existing between the multiple sequences of CMR data prevents a deep learning model trained on a specific contrast to be used on a different sequence. READ MORE