Thesis Topics

This list includes topics for potential bachelor or master theses, guided research, projects, seminars, and other activities. Search with Ctrl+F for desired keywords, e.g. ‘machine learning’ or others.

PLEASE NOTE: If you are interested in any of these topics, click the respective supervisor link to send a message with a simple CV, grade sheet, and topic ideas (if any). We will answer shortly.

Of course, your own ideas are always welcome!

Semantic Segmentation with Efficient Pixel-Transformers

Type of Work:

  • Master

Keywords:

  • efficient ML
  • semantic segmentation
  • vision transformer

Description:

Have you ever wondered how machines can “see” the world like we do, assigning labels to every single detail in an image? This field, called semantic segmentation, is crucial for tasks like self-driving cars and medical image analysis.

Transformers have conquered every aspect of machine learning. In particular in computer vision ViT [1] and variants of it are used. Because of their $\mathcal O(N^2)$ computational complexity in the length $N$ of the input sequence, transformers struggle with very long inputs. To combat this problem, an image is usually not fed into the transformer as is, but it is first cut up into $16 \times 16$ pixel segments, the so-called patches, which form the input sequence. This can cause problems for the task of semantic segmentation, since the goal is to capture pixel-level labels. Currently the workaround is to use complicated decoder networks to get back the pixel-level information from the patch-level output of a transformer model [2].

This project tackles this head-on, with the goal to use recent efficient transformer models that scale linearly $\mathcal O(N)$ or $\mathcal O(N \log N)$ to circumvent the decoding-problem by making the transformer use pixels instead of patches as the input. This way no complicated decoder is needed; a simpler, more powerful approach to segmentation using transformer models!

Dataset Distillation for Fast Proxy Evaluation of Generative Models

Type of Work:

  • Master

Keywords:

  • dataset distillation
  • generative models
  • text-to-image evaluation

Description:

In recent years, text-to-image generative models have advanced significantly. Traditionally, evaluating these models relies on generating thousands of images and comparing against a large dataset of real image. Evaluating these models is crucial but the substantial computational resources make regular performance monitoring challenging. Dataset distillation offers a solution by condensing the information into a smaller set of synthetic samples. This project aims to use dataset distillation to create a compact dataset that serves as a proxy for full dataset evaluation.

Spatial Explicit Machine Learning

Type of Work:

  • Guided Research
  • Master

Keywords:

  • Earth Observation
  • Machine Learning
  • Remote Sensing
  • Spatial Awareness Modeling
  • Spatial Transferability

Description:

Machine learning models designed and trained to work on a specific regions are not necessarily transferable to other spatially different region. Include a spatially explicit component is mandatory to differentiates behaviors and predictions according to spatial locations. However, it is no clear what is the best way to use this spatial information or which kind of models work best for spatial transferability. In this topic, global remote sensing data will be used for supervised learning in different Earth observation applications.

Feel free to reach out if you have any question or ideas regarding the topic.

Image Super-Resolution in both ways

Type of Work:

  • Bachelor
  • Guided Research

Keywords:

  • auto-encoder
  • deep learning
  • single image super-resolution

Description:

The goal of this project is to develop and evaluate a novel dual-decoder architecture for image super-resolution (SR) [1]. This architecture will utilize a single encoder to extract features from an input image, followed by two decoders: one trained to map the features to a low-resolution (LR) output, and the other to map the features to a high-resolution (HR) output. This approach aims to enhance the SR performance by leveraging the complementary learning objectives of both decoders. The goal of the work is to try different architectures and to analyze different loss formulations as well as the feature space learned by the encoder.

Applying TaylorShift to Transfomer-based Image Super-Resolution Models

Type of Work:

  • Master

Keywords:

  • deep learning
  • single image super-resolution
  • vision transformer

Description:

The aim of this project is to integrate the TaylorShift [1] attention mechanism into the SwinIR model to enhance the efficiency and performance of image super-resolution (SR) [2]. By leveraging the linear complexity of TaylorShift, we intend to improve the processing speed and reduce the memory footprint of SwinIR without compromising its high accuracy in generating high-resolution images from low-resolution inputs. Image super-resolution is a crucial task in computer vision that aims to enhance the resolution of images, making them clearer and more detailed. SwinIR (Swin Transformer for Image Restoration) has shown state-of-the-art performance in various image restoration tasks, including super-resolution. However, the quadratic complexity of its attention mechanism can be a bottleneck, especially for high-resolution images. TaylorShift, a novel reformulation of the Taylor softmax function, addresses this issue by reducing the complexity of the attention mechanism from quadratic to linear. This enables efficient processing of long sequences and high-resolution images while maintaining the ability to capture intricate token-to-token interactions.

Sherlock Holmes goes AI - Generative comics art of detective scenes and identikits

Type of Work:

  • Master

Keywords:

  • Bias in image generation models
  • Deep Learning Frameworks
  • Frontend visualization
  • Speech-To-Text, Text-to-Image Models
  • Transformers, Diffusion Models, Hugging Face

Description:

Sherlock Holmes is taking the statement of the witness. The witness is describing the appearance of the perpetrator and the forensic setting they still remember. Your task as the AI investigator will be to generate a comic sketch of the scene and phantom images of the accused person based on the spoken statement of the witness. For this you will use state-of-the-art transformers and visualize the output in an application. As AI investigator you will detect, qualify and quantify bias in the images which are produced by different generation models you have chosen.

Note:

This work is embedded in the DFKI KI4Pol lab together with the law enforcement agencies. The stories are fictional you will not work on true crime.

Requirements:

  • German level B1/2 or equivalent
  • Outstanding academic achievements
  • Motivational cover letter

Knowledge Graphs für das Immobilienmanagement

Type of Work:

  • Bachelor
  • Master

Keywords:

  • corporate memory
  • knowledge graph
  • ontologie

Description:

Das Management von Immobilien ist komplex und umfasst verschiedenste Informationsquellen und -objekte zur Durchführung der Prozesse. Ein Corporate Memory kann hier unterstützen in der Analyse und Abbildung des Informationsraums um Wissensdienste zu ermöglichen. Aufgabe ist es, eine Ontologie für das Immobilienmanagement zu entwerfen und beispielhaft ein Szenario zu entwickeln. Für die Materialien und Anwendungspartner sind gute Deutschkenntnisse erforderlich.

Fault and Efficiency Prediction in High Performance Computing

Type of Work:

  • Master Thesis

Keywords:

  • deep learning
  • event data modelling
  • survival modelling
  • time series

Description:

High use of resources are thought to be an indirect cause of failures in large cluster systems, but little work has systematically investigated the role of high resource usage on system failures, largely due to the lack of a comprehensive resource monitoring tool which resolves resource use by job and node. This project studies log data of the DFKI Kaiserslautern high performance cluster to consider the predictability of adverse events (node failure, GPU freeze), energy usage and identify the most relevant data within. The second supervisor for this work is Joachim Folz.

Data is available via Prometheus-compatible system:

Reference:

Feel free to reach out if the topic sounds interesting or if you have ideas related to this work. We can then brainstorm a specific research question together. Link to my personal website.

Construction & Application of Enterprise Knowledge Graphs in the E-Invoicing Domain

Type of Work:

  • Bachelor
  • Guided Research Project
  • Master

Keywords:

  • knowledge graphs
  • knowledge services
  • linked data
  • semantic web

Description:

In recent years knowledge graphs received a lot of attention as well in industry as in science. Knowledge graphs consist of entities and relationships between them and allow integrating new knowledge arbitrarily. Famous instances in industry are knowledge graphs by Microsoft, Google, Facebook or IBM. But beyond these ones, knowledge graphs are also adopted in more domain specific scenarios such as in e-Procurement, e-Invoicing and purchase-to-pay processes. The objective in theses and projects is to explore particular aspects of constructing and/or applying knowledge graphs in the domain of purchase-to-pay processes and e-Invoicing.

Anomaly detection in time-series

Type of Work:

  • Master
  • Project

Keywords:

  • cnn
  • explainability

Description:

Working on deep neural networks for making the time-series anomaly detection process more robust. An important aspect of this process is explainability of the decision taken by a network.

Time Series Forecasting Using transformer Networks

Type of Work:

  • Guided Research
  • Project

Keywords:

  • time series forecasting
  • transformer networks

Description:

Transformer networks have emerged as competent architecture for modeling sequences. This research will primarily focus on using transformer networks for forecasting time series (multivariate/ univariate) and may also involve fusing knowledge into the machine learning architecture.

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