75 citations · 115 across the 12 of their papers we have counts for
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cs.LG2019★ 3 cited
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
Daniel Stoller, Sebastian Ewert, Simon Dixon
Generative adversarial networks (GANs) have shown great success in applications such as image generation and inpainting. However, they typically require large datasets, which are o…
cs.LG2017
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
Daniel Stoller, Sebastian Ewert, Simon Dixon
The state of the art in music source separation employs neural networks trained in a supervised fashion on multi-track databases to estimate the sources from a given mixture. With…
cs.LG2016
Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation
Sebastian Ewert, Mark B. Sandler
Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, howeve…