LAMP

Category: Publications

  • Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery

    Grzegorz Rypeść, Daniel Marczak, Sebastian Cygert, Tomasz Trzciński, Bartłomiej Twardowski Read Full Paper → Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend on feature distillation to prevent forgetting the old knowledge. However, this strategy restricts the model’s ability to adapt and effectively distinguish new categories. To address […]

  • Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning

    Dipam Goswami, Albin Soutif-Cormerais, Yuyang Liu, Sandesh Kamath, Bartlomiej Twardowski, Joost van de Weijer Read Full Paper → Continual learning methods are known to suffer from catastrophic forgetting a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous tasks. Therefore to reduce potential drift in the feature extractor […]

  • Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning

    Alex Gomez-Villa, Bartlomiej Twardowski, Kai Wang, Joost van de Weijer Read Full Paper → Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL methods using SSL can learn high-quality representations without any labels, but with a notable performance drop when learning on a […]

  • Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-Free Continual Learning

    Filip Szatkowski, Mateusz Pyla, Marcin Przewięźlikowski, Sebastian Cygert, Bartłomiej Twardowski, Tomasz Trzciński Read Full Paper → In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KD-based methods are successfully used in CIL, but they often struggle to regularize the model without access […]

  • 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 […]

  • FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

    Dipam Goswami, Yuyang Liu, Bartłomiej Twardowski, Joost van de Weijer Read Full Paper → Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained much attention. In […]

  • Two papers accepted at Neural Information Processing Systems (NeurIPS) 2023

  • IterInv: Iterative Inversion for Pixel-Level T2I Models

    Chuanming Tang, Kai Wang, Joost van de Weijer Read Full Paper → Large-scale text-to-image diffusion models have been a ground-breaking development in generating convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques predominantly hinge on […]