584 citations · 2.3k across the 78 of their papers we have counts for
14 papers · 1 filter
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
Kai Wang, Fei Yang, Shiqi Yang +2
Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincin…
A Comprehensive Empirical Evaluation on Online Continual Learning
Albin Soutif--Cormerais, Antonio Carta, Andrea Cossu +4
Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum a…
Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning
Alex Gomez-Villa, Bartlomiej Twardowski, Kai Wang +1
Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL met…
Continual Evidential Deep Learning for Out-of-Distribution Detection
Eduardo Aguilar, Bogdan Raducanu, Petia Radeva +1
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out a…
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
Dipam Goswami, Yuyang Liu, Bartłomiej Twardowski +1
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.…
ScrollNet: Dynamic Weight Importance for Continual Learning
Fei Yang, Kai Wang, Joost van de Weijer
The principle underlying most existing continual learning (CL) methods is to prioritize stability by penalizing changes in parameters crucial to old tasks, while allowing for plast…
Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection
Liu Yuyang, Cong Yang, Goswami Dipam +2
In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting.…
Improving Online Continual Learning Performance and Stability with Temporal Ensembles
Albin Soutif--Cormerais, Antonio Carta, Joost Van de Weijer
Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an onlin…
Density Map Distillation for Incremental Object Counting
Chenshen Wu, Joost van de Weijer
We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach…
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning
Antonio Carta, Andrea Cossu, Vincenzo Lomonaco +2
Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs.…
3D-Aware Multi-Class Image-to-Image Translation with NeRFs
Senmao Li, Joost van de Weijer, Yaxing Wang +3
Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aw…
StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing
Senmao Li, Joost van de Weijer, Taihang Hu +5
A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.They either finetune the model, or invert the…
ICICLE: Interpretable Class Incremental Continual Learning
Dawid Rymarczyk, Joost van de Weijer, Bartosz Zieliński +1
Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on b…
Towards Label-Efficient Incremental Learning: A Survey
Mert Kilickaya, Joost van de Weijer, Yuki M. Asano
The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assume…