3 papers
cs.SD2019
Spectrogram Feature Losses for Music Source Separation
Abhimanyu Sahai, Romann Weber, Brian McWilliams
In this paper we study deep learning-based music source separation, and explore using an alternative loss to the standard spectrogram pixel-level L2 loss for model training. Our ma…
stat.ML2018
Disentangled Dynamic Representations from Unordered Data
Leonhard Helminger, Abdelaziz Djelouah, Markus Gross +1
We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input. Our approach exploits regularities in sequential data t…
cs.CV2018
Unsupervised Deep Representations for Learning Audience Facial Behaviors
Suman Saha, Rajitha Navarathna, Leonhard Helminger +1
In this paper, we present an unsupervised learning approach for analyzing facial behavior based on a deep generative model combined with a convolutional neural network (CNN). We jo…