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.