Index
Feature Extraction and Dimensionality Reduction Techniques for Assessing Model Similarity in Large-Scale 3D CAD Datasets
Work description
The research presented in this thesis explores the application of feature extraction and dimensionality reduction techniques to assess model similarity within large-scale 3D CAD datasets. It investigates how different geometric and topological descriptors can be quantified and utilized to measure the similarity between complex 3D models. Therefore, the study employs advanced machine learning algorithms to analyze and cluster 3D data, facilitating a better understanding of model characteristics and relationships.
During the thesis, the following questions should be considered:
- What metrics can effectively quantify the variance in a training dataset?
- How does the variance within a training set impact the neural network’s ability to generalize to new, unseen data?
- What is the optimal balance of diversity and specificity in a training dataset to maximize NN performance?
- How can training datasets be curated to include a beneficial level of variance without compromising the quality of the neural network’s output?
- What methodologies can be implemented to systematically adjust the variance in training data and evaluate its impact on NN generalization?
Prerequisites
Applicants should have a solid background in machine learning and deep learning, with strong technical skills in Python and experience with PyTorch. Candidates should also possess the capability to work independently and have a keen interest in exploring the theoretical aspects of neural network training.
For your application, please send your transcript of record.
Definition und Implementierung einer prototypischen Smart Home Schnittstelle für ein cloudbasiertes Energiemanagementsystem
Transformers vs. Convolutional Networks for 3D segmentation in industrial CT data
The current state of the art for segmentation in industrial CT are oftentimes CNNs.
Transformer based models are sparsely used.
Therefore, this project wants to compare the semantic segmentation performance of transformers (that include global context into segmentation), pure convolutional neural networks (that use local context) and combined methods (like this one: https://doi.org/10.1186/s12911-023-02129-z) on an industrial CT dataset of shoes like in this study: https://doi.org/10.58286/27736 .
Only available as Bachelors thesis / Research Project
Developing and Evaluating Image Similarity Metrics for Enhanced Classification Performance in 2D Datasets
Work description
This thesis focuses on the development and evaluation of novel image similarity metrics tailored for 2D datasets, aiming to improve the effectiveness of classification algorithms. By integrating active learning methods, the research seeks to refine these metrics dynamically through iterative feedback and validation. The work involves extensive testing and validation across diverse 2D image datasets, ensuring robustness and applicability in varied scenarios.
The following questions should be considered:
- What metrics can effectively quantify the variance in a training dataset?
- How does the variance within a training set impact the neural network’s ability to generalize to new, unseen data?
- What is the optimal balance of diversity and specificity in a training dataset to maximize NN performance?
- How can training datasets be curated to include a beneficial level of variance without compromising the quality of the neural network’s output?
- What methodologies can be implemented to systematically adjust the variance in training data and evaluate its impact on NN generalization?
Prerequisites
Applicants should have a solid background in machine learning and deep learning, with strong technical skills in Python and experience with PyTorch. Candidates should also possess the capability to work independently and have a keen interest in exploring the theoretical aspects of neural network training.
For your application, please send your transcript of record.