207 citations · 253 across the 25 of their papers we have counts for
9 papers · 1 filter
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…
AudioCLIP: Extending CLIP to Image, Text and Audio
Andrey Guzhov, Federico Raue, Jörn Hees +1
In the past, the rapidly evolving field of sound classification greatly benefited from the application of methods from other domains. Today, we observe the trend to fuse domain-spe…
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…
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style
Stanislav Frolov, Avneesh Sharma, Jörn Hees +3
Conditional image synthesis from layout has recently attracted much interest. Previous approaches condition the generator on object locations as well as class labels but lack fine-…
RECol: Reconstruction Error Columns for Outlier Detection
Jörn Hees, Dayananda Herurkar, Mario Meier
Detecting outliers or anomalies is a common data analysis task. As a sub-field of unsupervised machine learning, a large variety of approaches exist, but the vast majority treats t…
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…
Adversarial Text-to-Image Synthesis: A Review
Stanislav Frolov, Tobias Hinz, Federico Raue +2
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way fo…