LAMP

Category: ICLR

  • Divide and not forget: Ensemble of selectively trained experts in Continual Learning

    Grzegorz Rypeść, Sebastian Cygert, Valeriya Khan, Tomasz Trzciński, Bartosz Zieliński, Bartłomiej Twardowski Read Full Paper → Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are […]

  • Get What You Want, Not What You Don’t: Image Content Suppression for Text-to-Image Diffusion Models

    Senmao Li, Joost van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, Jian Yang Read Full Paper → Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient data is available in the first task […]

  • Elastic Feature Consolidation for Cold Start Exemplar-free Incremental Learning

    Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais, Joost van de Weijer, Andrew D. Bagdanov Read Full Paper → Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient data is available in the first task to learn […]

  • Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training

    Simone Zini, Alex Gomez-Villa, Marco Buzzelli, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer Read Full Paper → Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze […]

  • One paper accepted at International Conference on Learning Representations (ICLR) 2022

  • Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data

    Yaxing Wang, Joost van de weijer, Lu Yu, SHANGLING JUI Read Full Paper → Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data. Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model […]

  • On-the-fly Network Pruning for Object Detection

    Marc Masana, Joost van de Weijer, Andrew D. Bagdanov Read Full Paper → Object detection with deep neural networks is often performed by passing a few thousand candidate bounding boxes through a deep neural network for each image. These bounding boxes are highly correlated since they originate from the same image. In this paper we investigate how […]