Sebastian Hönel

Sebastian Hönel

Postdoctoral Fellow
Department of Computer Science and Media Technology Faculty of Technology
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About Me

I am currently a postdoctoral researcher and co-Pi in the project In-line visual inspection using unsupervised learning.

  • You will find me in building D, room D2254.
  • I have a calendar where you can find my public appearances, talks, meetings, office hours, and other public activities at bit.ly/2XY1RZ1.

Teaching

I am usually involved in teaching courses around (Deep) Machine Learning (e.g., 4DV652, 4DV660, 4DV661).

I was previously a teaching assistant in courses for agile product development, both first- and second-level cycles (e.g., 1DV508, 4DV611).

Research

Previously, I emphasized the usage of Machine- and Deep Learning, Optimization, as well as distributed computing for the analysis, calibration, and optimization of models that are related to software applications and -processes. I was (and still am) interested in obtaining quantitative data from software evolutionary processes and applying it through models used for the greater goal of organizational learning.

 

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Currently, I attempt to apply and improve models for unsupervised and zero-/few-shot learning for the detection of manufacturing defects and other material anomalies. I am particularly interested in Deep Density Estimation (e.g., Normalizing Flows) and Representation Learning. In greater detail, you find what interests me most below.

I usually have concrete problems in some of the following that need solving and are suitable for collaborations or writing a degree project. If you are interested in any of the following, please get in touch!

Anomaly Detection :

  • Industrial anomaly detection and -localization.
  • One-class classification and outlier exposure.
  • Reconstruction vs. classification vs. density estimation methods.
  • Contrastive learning and its applications.
  • Robustness and invariance in anomaly detection.

Normalizing Flows (NF) :

  • Density estimation using Normalizing Flows.
  • Challenges and limitations in using NFs for OOD detection.
  • Improvements and adjustments in NF architectures (e.g., coupling layers, attention mechanisms).
  • Comparison with other probabilistic and generative models.

Feature Extraction and Representation Learning :

  • Using pre-trained feature extractors (FEs) like Vision Transformers (ViTs) and ordinary convolutional (equivariant) networks.
  • Hierarchical and multi-scale feature extraction.
  • The role of semantic features versus low-level features.
  • Self-supervised learning to improve model robustness and uncertainty.

Architectural Innovations :

  • Autoencoders, especially variations like stacked convolutional AEs and zero-bias AEs.
  • Hybrid models with deep and invertible features.
  • Specialized AutoEncoders (AEs) and custom losses (e.g., Mutual Information)

Methodological Considerations :

  • Evaluation metrics (e.g., AUROC, MI, WAIC, MMD, Typicality).
  • Synthetic and variational data augmentation, as well as synthesizing points of interest (e.g., anomalies, novelties)
  • Proxy tasks
  • Runtime regularization (noise & temperature scaling; ratio models; etc.)

Key Algorithms and Methods :

  • Independent Component Analysis (ICA) and Transform-based methods.
  • Change-of-Variable (Gaussianization) and probability integral transform.
  • The use of various loss functions (e.g., focal loss, margin ranking loss).
  • Transfer learning and the importance of auxiliary data.

 

Publications

Selected publications

Article in journal (Refereed)

Conference paper (Refereed)

Dataset (Refereed)

Other (Refereed)

Conference paper (Other academic)

Doctoral thesis, comprehensive summary (Other academic)

Other (Other academic)

Licentiate thesis, comprehensive summary (Other academic)