LAMP

Category: NeurIPS

  • DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

    Yaxing Wang, Lu Yu, Joost van de Weijer Read Full Paper → Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Therefore, in this work, we propose […]

  • Memory Replay GANs: learning to generate images from new categories without forgetting

    Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, Bogdan Raducanu Read Full Paper → Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. […]

  • Image-to-image translation for cross-domain disentanglement

    Abel Gonzalez-Garcia, Joost van de Weijer, Yoshua Bengio Read Full Paper → Deep image translation methods have recently shown excellent results, outputting high-quality images covering multiple modes of the data distribution. There has also been increased interest in disentangling the internal representations learned by deep methods to further improve their performance and achieve a finer control. In […]

  • Ensembles of Generative Adversarial Networks

    Yaxing Wang, Lichao Zhang, Joost van de Weijer Read Full Paper → Ensembles are a popular way to improve results of discriminative CNNs. The combination of several networks trained starting from different initializations improves results significantly. In this paper we investigate the usage of ensembles of GANs. The specific nature of GANs opens up several new ways […]

  • Invertible Conditional GANs for image editing

    Guim Perarnau, Joost van de Weijer, Bogdan Raducanu, Jose M. Álvarez Read Full Paper → Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate […]