46 citations · 49 across the 2 of their papers we have counts for
2 papers
cs.CV2017★ 3 cited
Deep Convolutional Neural Networks as Generic Feature Extractors
Lars Hertel, Erhardt Barth, Thomas Käster +1
Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve conv…
cs.CL2017★ 46 cited
A Hybrid Convolutional Variational Autoencoder for Text Generation
Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth
In this paper we explore the effect of architectural choices on learning a Variational Autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for…