7 papers
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…
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…
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks
Sebastian Palacio, Philipp Engler, Jörn Hees +1
Classification problems solved with deep neural networks (DNNs) typically rely on a closed world paradigm, and optimize over a single objective (e.g., minimization of the cross-ent…
Revisiting Sequence-to-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory
Fatemeh Azimi, Benjamin Bischke, Sebastian Palacio +3
Video Object Segmentation (VOS) is an active research area of the visual domain. One of its fundamental sub-tasks is semi-supervised / one-shot learning: given only the segmentatio…
P NP, at least in Visual Question Answering
Shailza Jolly, Sebastian Palacio, Joachim Folz +3
In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets. One of the most widely-used of these is the…
What do Deep Networks Like to See?
Sebastian Palacio, Joachim Folz, Jörn Hees +3
We propose a novel way to measure and understand convolutional neural networks by quantifying the amount of input signal they let in. To do this, an autoencoder (AE) was fine-tuned…