Index

Clustering of HPC jobs using Unsupervised Machine Learning on job performance metric time series data

Deep Learning-based Matching of Chest X-Ray Scans

The use of human identification has become an increasingly important factor over the past years, with
facial recognition being potentially the most common form used in daily life. But the face is not the
only biometric identifier that can be used as a feature for identification. In this work, we will investigate
chest X-rays as biometric identifiers. If they were proven to be viable, it would for example allow
identification post mortem, where common techniques currently have shortcomings [1]. Also, a success
in such a way of identification may have far-reaching consequences and implications concerning data
protection and anonymity in the medical field.
In pattern recognition, the use of deep learning has proven to be successful in improving or even
replacing classical methods entirely. To test the limits of what is currently possible, a neural network
will be created that takes in two different x-ray scans as inputs and outputs a score measuring their
similarity.
To increase the chances of success, a registration step will be incorporated in the preprocessing step. It
will be be implemented as a neural network layer, as this has proven to be effective in the past [2].
The thesis consists of the following milestones:
• Testing out the capabilities of different network architectures concerning the task of finding
matches in chest X-Ray scans
• Further enhancing the functionality by incorporating a layer into the network that is capable of
affine registrations, e. g. by means of a spatial transformer network [3]
The implementation should be done in Python.

References
[1] Ryudo Ishigami, Thi Thi Zin, Norihiro Shinkawa, and Ryuichi Nishii. Human identification using x-ray
image matching. In Proceedings of The International MultiConference of Engineers and Computer Scientists
2017, volume 1, pages 415–418, 2017.
[2] Grant Haskins, Uwe Kruger, and Pingkun Yan. Deep learning in medical image registration: a survey.
Machine Vision and Applications, 31(1–2), Jan 2020.
[3] Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu. Spatial transformer networks.
In Advances in Neural Information Processing Systems 28, pages 2017–2025. Curran Associates, Inc., 2015.

Start, follow, read, stop: Incorporating new steps into end-to-end full-page handwriting recognition method

In this work, new steps are incorporated into a known offline recognition method [1] as an attempt to
improve the transcription of degraded and poor-quality historical documents. The previously proposed
model consists of three components:
1. Start-of-line (SOL)
This network predicts the starting points of lines, together with an indication of the size and
direction of the handwriting.
2. Line-follower (LF)
Given a starting point, the LF network follows the handwriting line in incremental steps and
outputs a dewarped line image that is suitable for text recognition purposes.
3. Handwriting recognition (HWR)
After having the LF network produce several normalized line images, these can then be fed to a
CNN-LSTM HWR network [2] to produce transcriptions of the detected lines.
The method performed well on warped lines and has the advantage of outputting polygonal regions
instead of bounding boxes [3], but it still has several shortcomings, specially when considering
documents where unrelated pieces of information are frequently horizontally adjacent to one another.
It cannot detect and adapt to changes in handwriting size either, relying solely on the initial prediction
made by the SOL network to extract lines.
Modifications are to be made to the network architecture of the model in order to address these
shortcomings, and the thesis would then consist of the following milestones:
• Extending the SOL network architecture in order to include End-of-Line (EOL) detection.
• Modifying the LF network architecture to capture variations in handwriting size.
• Applying the LF network backwards from EOL predictions and finding an effective way of
merging both line information.
• Evaluating performance on historical full page datasets.
• Further experiments regarding procedure and network architecture.

The implementation should be done in Python.

References
[1] Davis B. Barrett W. Price B. Cohen S. Wigington C., Tensmeyer C. Start, follow, read: End-to-end full-page
handwriting recognition. Computer Vision – European Conference on Computer Vision 2018 (ECCV) pages
372-388, 2018.
[2] Stewart S. Davis B. Barrett W. Price B. Cohen S. Wigington, C. Data augmentation for recognition of
handwritten words and lines using a cnn-lstm network. 14th International Conference on Document Analysis
and Recognition (ICDAR) pp. 639–645, 2017.
[3] Wolf C. Moysset B., Kermorvant C. Full-page text recognition: Learning where to start and when to stop.
14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017.

Development of a pre-processing/simulation Framework for Multi-Channel Audio Signals

The goal of this thesis is to develop a framework that simulates multi-channel audio signals in a 2D/3D environment for hearing aids. For this purpose, existing head related transfer functions (HRTFs) will be used to simulate direction and hearing aid microphone characteristics. Furthermore, source movement as well as microphone movement and rotation will be implemented. The latter is mandatory for hearing aids, since especially head rotation might change the relative direction of the different sources significantly. The framework will be able to simulate multiple speakers as well as multiple noise sources. To calculate a clean speech target, a provided reference beamformer will be used on the target speech only, neglecting noise and non target speakers. Optionally, an opening angle that defines the target directions can be used to extract the clean speech targets. As a second optional aspect, room environment including absorption and reverberation will be simulated. Therefore, a reference implementation can be used.

Semantic Segmentation of the Human Eye for Driver Monitoring

Extension of the Lottery Ticket Hypothesis for Saving Computational Cost and Energy

Many state-of-the-art neural networks have millions of parameters, e.g. VGG’s smallest configuration has 133 million parameters[1]. They achieve high test and training accuracies but require a high computational cost. Since powerful hardware exists, coping with that computational cost is possible but very inefficient. To countereffect these inefficiencies network pruning can be applied to decrease the size of the neural networks. Because the resulting accuracies after pruning did not match the ones of the original network for many years but in contrast hardware got more performing and cheaper, pruning was thought not to be optimal and the trend of constructing neural networks went towards creating big networks that perform consistently well on high performing hardware.

In 2019 The Lottery Ticket Hypothesis LTH [2] was introduced as a new approach for pruning neural networks. Using a binary mask the lowest weights are selectively set to zero and therefore connections are removed from the network. It suggests that fully-connected and convolutional neural networks can be iteratively pruned into a sparse subnetwork such that the parameter count can be reduced by over 90% while the number of iterations in training is at max as high as the original network’s and the test accuracy meets or even exceeds the original one. This paper opened up a large area of discussion where on the one hand some papers do not find improvements of the LTH over random initialization [3], whereas on the other hand some even found insights to why the approach works well [4].

A drawback of the lottery ticket hypothesis is however that the network’s structure of neurons still stays the same and no neurons are removed to decrease computational cost. The goal of this thesis is to investigate whether neural network pruning by reducing the number of neurons based on the idea of the lottery ticket hypothesis is possible. Additional goals would be to compute the amount of energy savings [5], compare masks and structures created by different datasets and optimizers for the same network to acquire potentially deeper insights and see if the idea of a supermask [4] also exists for the approach of neuron pruning.

The thesis aims to achieve following goals:
• Extending the lottery ticket hypothesis by actually removing neurons, instead of using a binary mask.
• Comparing the accuracies and network size to the original thesis on different datasets.

Additional investigations should be taken in:
• Comparing the network structures and masks of different datasets and optimizers.
• Compute the amount of energy savings. [5]
• Incorporate the idea of a supermask [4] to the approach of neuron pruning.
• Investigate pruning procedures aiming at removing enurons,instead of cutting connections.

[1] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2015.
[2] Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Training pruned neural networks. CoRR, abs/1803.03635, 2018.
[3] Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell. Rethinking the value of network pruning. CoRR, abs/1810.05270, 2018.
[4] Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski. Deconstructing lottery tickets: Zeros, signs, and the supermask. CoRR, abs/1905.01067, 2019.
[5] Tien-Ju Yang, Yu-Hsin Chen, and Vivienne Sze. Designing energy-efficient convolutional neural networks using energy-aware pruning. CoRR, abs/1611.05128, 2016.

Multi-task Learning for Historical Handwritten Document Classification

In the Competition on Image Retrieval for Historical Handwritten Documents 2019 [1], several methods
have been proposed to identify the writer of a document. Although most of the proposed methods
are based on feature descriptors and traditional machine learning techniques, the deep learning based
methods are emerging in the field of historical document classification. At the same time, other deep
learning based methods are dominating the image retrieval and classification task.
Multi-task learning (MTL) is an approach to learning multiple tasks at the same time using one neural
network. This approach has not been used for historical handwritten document classification. Over
the years, MTL has been applied to many fields, not only for computer vision, but also to speech
processing, bioinformatics, etc. , to boost the performance [2]. In context of computer vision, MTL is
used to detect facial landmarks to improve the performance of expression recognition [3]. Furthermore,
a convolutional neuronal network has been proposed for pose estimation with some auxiliary tasks: for
example body part detection [4].
In this work, we will investigate the approach with neural networks using multi-task learning for
historical handwritten document classification. We will use the online published datasets from previous
competitions for training and testing.
We will implement two multi-task neural networks, one should focus on writer identification with the
auxiliary task: binarization. The performance of this multi-task learning algorithm will be evaluated
using the datasets from the ICDAR2017 Competition on Historical Document Writer Identification
(Historical-WI) [5]. The other neural network should focus on dating and style classification and
the performance of this multi-task learning algorithm will be evaluated using the datasets from the
ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script [6].
The thesis consists of the following milestones:
• Review of the related work and methods of historical handwritten document classification
• Implemention of two neural networks for multi-task learning for:
– writer identification and binarization
– date classification and script type classification
• Evaluation the results of these two multi-task neural networks
• Comparison of this approach to current document classification approaches
• Examination and discussion about whether a multi-task neural network is useful for document
classification.
The implementation should be done in Pytorch.

[1] Vincent Christlein, Anguelos Nicolaou, Mathias Seuret, Dominique Stutzmann, and Andreas Maier. Icdar
2019 competition on image retrieval for historical handwritten documents. In International Conference on
Document Analysis and Recognition (ICDAR), 2019.
[2] Yu Zhang and Qiang Yang. A survey on multi-task learning. arXiv preprint arXiv:1707.08114, 2017.
[3] Terrance Devries, Kumar Biswaranjan, and Graham W. Taylor. Multi-task learning of facial landmarks and
expression. In 2014 Canadian Conference on Computer and Robot Vision, pages 98–103. IEEE, 2014.
[4] Sijin Li, Zhi-Qiang Liu, and Antoni B. Chan. Heterogeneous multi-task learning for human pose estimation
with deep convolutional neural network. In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition Workshops, pages 482–489, 2014.
[5] Stefan Fiel, Florian Kleber, Markus Diem, Vincent Christlein, Georgios Louloudis, Stamatopoulos Nikos,
and Basilis Gatos. Icdar2017 competition on historical document writer identification (historical-wi). In
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), volume 1,
pages 1377–1382. IEEE, 2017.
[6] Florence Cloppet, Veronique Eglin, Marlene Helias-Baron, Cuong Kieu, Nicole Vincent, and Dominique
Stutzmann. Icdar2017 competition on the classification of medieval handwritings in latin script. In 2017
14th IAPR International Conference on Document Analysis and Recognition (ICDAR), volume 1, pages
1371–1376. IEEE, 2017.