7 citations · 11 across the 7 of their papers we have counts for
21 papers
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data
Timur Sattarov, Dayananda Herurkar, Jörn Hees
Recent advances in Explainable AI (XAI) increased the demand for deployment of safe and interpretable AI models in various industry sectors. Despite the latest success of deep neur…
DT2I: Dense Text-to-Image Generation from Region Descriptions
Stanislav Frolov, Prateek Bansal, Jörn Hees +1
Despite astonishing progress, generating realistic images of complex scenes remains a challenging problem. Recently, layout-to-image synthesis approaches have attracted much intere…
Spatial Transformer Networks for Curriculum Learning
Fatemeh Azimi, Jean-Francois Jacques Nicolas Nies, Sebastian Palacio +3
Curriculum learning is a bio-inspired training technique that is widely adopted to machine learning for improved optimization and better training of neural networks regarding the c…
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks
Fatemeh Azimi, Federico Raue, Joern Hees +1
Spatial Transformer Networks (STN) can generate geometric transformations which modify input images to improve the classifier's performance. In this work, we combine the idea of ST…
XAI Handbook: Towards a Unified Framework for Explainable AI
Sebastian Palacio, Adriano Lucieri, Mohsin Munir +3
The field of explainable AI (XAI) has quickly become a thriving and prolific community. However, a silent, recurrent and acknowledged issue in this area is the lack of consensus re…
ESResNe(X)t-fbsp: Learning Robust Time-Frequency Transformation of Audio
Andrey Guzhov, Federico Raue, Jörn Hees +1
Environmental Sound Classification (ESC) is a rapidly evolving field that recently demonstrated the advantages of application of visual domain techniques to the audio-related tasks…