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Saliency Diversified Deep Ensemble for Robustness to Adversaries
Alex Bogun, Dimche Kostadinov, Damian Borth
Deep learning models have shown incredible performance on numerous image recognition, classification, and reconstruction tasks. Although very appealing and valuable due to their pr…
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
Hamed Hemati, Marco Schreyer, Damian Borth
International audit standards require the direct assessment of a financial statement's underlying accounting journal entries. Driven by advances in artificial intelligence, deep-le…
Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Konstantin Schürholt, Dimche Kostadinov, Damian Borth
Self-Supervised Learning (SSL) has been shown to learn useful and information-preserving representations. Neural Networks (NNs) are widely applied, yet their weight space is still…
Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning
Shijun Wang, Dimche Kostadinov, Damian Borth
Voice Conversion (VC) for unseen speakers, also known as zero-shot VC, is an attractive research topic as it enables a range of applications like voice customizing, animation produ…
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks
Marco Schreyer, Timur Sattarov, Damian Borth
International audit standards require the direct assessment of a financial statement's underlying accounting transactions, referred to as journal entries. Recently, driven by the a…
Heterogeneous Ensemble for ESG Ratings Prediction
Tim Krappel, Alex Bogun, Damian Borth
Over the past years, topics ranging from climate change to human rights have seen increasing importance for investment decisions. Hence, investors (asset managers and asset owners)…
Learning Interpretable Concept Groups in CNNs
Saurabh Varshneya, Antoine Ledent, Robert A. Vandermeulen +4
We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept…
Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
Linus Scheibenreif, Michael Mommert, Damian Borth
Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problemati…
Power Plant Classification from Remote Imaging with Deep Learning
Michael Mommert, Linus Scheibenreif, Joëlle Hanna +1
Satellite remote imaging enables the detailed study of land use patterns on a global scale. We investigate the possibility to improve the information content of traditional land us…
NoiseVC: Towards High Quality Zero-Shot Voice Conversion
Shijun Wang, Damian Borth
Voice conversion (VC) is a task that transforms voice from target audio to source without losing linguistic contents, it is challenging especially when source and target speakers a…
Continual Speaker Adaptation for Text-to-Speech Synthesis
Hamed Hemati, Damian Borth
Training a multi-speaker Text-to-Speech (TTS) model from scratch is computationally expensive and adding new speakers to the dataset requires the model to be re-trained. The naive…