LAMP

Category: Publications

  • Slimmable Compressive Autoencoders for Practical Neural Image Compression

    Fei Yang, Luis Herranz, Yongmei Cheng, Mikhail G. Mozerov Read Full Paper → Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computationally demanding and generally optimized for a single rate, limiting their practical use. Focusing on practical image compression, we propose slimmable compressive autoencoders […]

  • Bookworm continual learning: beyond zero-shot learning and continual learning

    Kai Wang, Luis Herranz, Anjan Dutta, Joost van de Weijer Read Full Paper → We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) […]

  • Disentanglement of Color and Shape Representations for Continual Learning

    David Berga, Marc Masana, Joost Van de Weijer Read Full Paper → We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our […]

  • On Class Orderings for Incremental Learning

    Marc Masana, Bartłomiej Twardowski, Joost van de Weijer Read Full Paper → The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incrementally learned classifiers. We propose a method to compute various orderings for a dataset. The orderings are derived […]

  • RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning

    Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer Read Full Paper → Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper […]

  • 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 […]

  • Mix and match networks: cross-modal alignment for zero-pair image-to-image translation

    Yaxing Wang, Luis Herranz, Joost van de Weijer Read Full Paper → This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match […]

  • Generative Feature Replay For Class-Incremental Learning

    Xialei Liu, Chenshen Wu, Mikel Menta, Luis Herranz, Bogdan Raducanu, Andrew D. Bagdanov, Shangling Jui, Joost van de Weijer Read Full Paper → Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. […]

  • Semi-supervised Learning for Few-shot Image-to-Image Translation

    Yaxing Wang, Salman Khan, Abel Gonzalez-Garcia, Joost van de Weijer, Fahad Shahbaz Khan Read Full Paper → In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot […]

  • Semantic Drift Compensation for Class-Incremental Learning

    Lu Yu, Bartłomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, Joost van de Weijer Read Full Paper → Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer […]