ÖZYEĞİN UNIVERSITY
GRADUATE SCHOOL OF INDUSTRIAL
ENGINEERING
DS 502.A: Introduction to Operations Research Techniques in Data
Science
PROJECT PROPOSAL
Hierarchical Generative Models and
Regularization Techniques in Machine
Learning
DEMBA SOW
1. Introduction
The field of machine learning has advanced significantly with the introduction of
hierarchical models that improve data representation, generalization, and interpretability.
This project aims to explore two key research areas: (1) Hierarchical Mixtures of Generators
(HMoG) in Generative Adversarial Networks (GANs) and (2) Dropout Regularization in
Hierarchical Mixture of Experts (HMoE). These methods provide solutions to common
challenges such as mode collapse in GANs and overfitting in hierarchical models.
2. Objectives
1. Implement and analyze Hierarchical Mixtures of Generators (HMoG) in GANs to
improve sample quality and diversity.
2. Develop a Dropout Regularization Method for HMoE to enhance generalization
and prevent overfitting.
3. Evaluate the e ectiveness of these methods on publicly available datasets such as
MNIST, CIFAR-10, and CelebA.
4. Compare performance against baseline models to assess improvements in training
stability, interpretability, and computational e iciency.
3. Literature Review
3.1 Hierarchical Mixtures of Generators (HMoG) in GANs
Reference: Alper Ahmetoğlu, Ethem Alpaydın, Hierarchical Mixtures of Generators
for Adversarial Learning, ICPR 2020.
Summary: Introduces a tree-structured multi-generator GAN to improve diversity in
generated samples and mitigate mode collapse.
Implementation Considerations:
o Multi-generator GAN architecture using PyTorch/TensorFlow.
o Wasserstein loss for adversarial training.
o Experimentation on image datasets (MNIST, FashionMNIST, CelebA, etc.).
3.2 Dropout Regularization in Hierarchical Mixture of Experts (HMoE)
Reference: Ozan Irsoy, Ethem Alpaydın, Dropout Regularization in Hierarchical
Mixture of Experts, Neurocomputing 2021.
Summary: Proposes a tree-structured dropout regularization method to reduce
overfitting and improve model interpretability.
Implementation Considerations:
o Applying dropout at decision nodes instead of individual neurons.
o Training with scikit-learn or PyTorch.
o Evaluating model performance on MNIST, CIFAR-10, and sentiment
classification datasets.
4. Methodology
1. Data Collection:
o Use benchmark datasets such as MNIST, CIFAR-10, CelebA, and SSTB
(Sentiment Treebank).
2. Model Development:
o Implement HMoG GAN using multiple generators in a hierarchical
structure.
o Develop HMoE with structured dropout regularization.
3. Evaluation Metrics:
o Frechet Inception Distance (FID) for GAN evaluation.
o Cross-validation accuracy and generalization gap for HMoE models.
o Computational e iciency and scalability analysis.
4. Comparison with Baselines:
o Standard GANs vs. HMoG.
o Fully connected models vs. HMoE with dropout.
5. Expected Outcomes
Improved sample diversity and stability in GAN-generated outputs using HMoG.
Reduced overfitting and better generalization in hierarchical models via
structured dropout.
A comparative study showcasing the advantages of hierarchical learning in machine
learning models.
6. Tools & Technologies
Programming Languages: Python
Libraries & Frameworks: PyTorch, TensorFlow, scikit-learn, NumPy, OpenCV
Development Environment: Jupyter Notebook, VS Code
Hardware Requirements: GPU-enabled system for deep learning models
7. Conclusion
This project aims to advance the field of hierarchical learning by addressing key challenges
in GANs and hierarchical decision models. By implementing and evaluating these
approaches, we hope to contribute valuable insights into improving generative modeling
and hierarchical learning architectures.
8. References
Alper Ahmetoğlu, Ethem Alpaydın. Hierarchical Mixtures of Generators for
Adversarial Learning. ICPR 2020.
Ozan Irsoy, Ethem Alpaydın. Dropout Regularization in Hierarchical Mixture of
Experts. Neurocomputing 2021.
