Closing the Training/Inference Gap for Deep Attractor Networks

Cyril Cadoux, Stefan Uhlich, Marc Ferras +1

This paper improves the deep attractor network (DANet) approach by closing its gap between training and inference. During training, DANet relies on attractors, which are computed f…