58 citations · 163 across the 41 of their papers we have counts for
21 papers · 1 filter
On the Limits of Representation-Based Test-Time Adaptation for Multimodal LLMs
Mikołaj Piórczyński, Wojciech Masarczyk, Damian Sójka +2
Vision-language models suffer significant accuracy drops under common corruptions. While test-time adaptation (TTA) successfully mitigates these shifts in CLIP-like contrastive mod…
Rethinking the Teacher-Student Framework for Test-Time Adaptation
Damian Sójka, Marc Masana, Bartłomiej Twardowski +1
Test-Time Adaptation (TTA) has recently emerged as a promising strategy that allows the adaptation of pre-trained models to changing data distributions at deployment time, without…
Online Continual Learning with Dynamic Label Hierarchies
Xinrui Wang, Shao-Yuan Li, Bartłomiej Twardowski +2
Online Continual Learning (OCL) aims to learn from endless non\text{-}stationary data streams, yet most existing methods assume a flat label space and overlook the hierarchical org…
Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses
Stanisław Pawlak, Jan Dubiński, Daniel Marczak +1
Model merging (MM) recently emerged as an effective method for combining large deep learning models. However, it poses significant security risks. Recent research shows that it is…
No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces
Daniel Marczak, Simone Magistri, Sebastian Cygert +3
Model merging integrates the weights of multiple task-specific models into a single multi-task model. Despite recent interest in the problem, a significant performance gap between…
Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
Dipam Goswami, Simone Magistri, Kai Wang +3
Using pre-trained models has been found to reduce the effect of data heterogeneity and speed up federated learning algorithms. Recent works have explored training-free methods usin…
Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning
Grzegorz Rypeść, Sebastian Cygert, Tomasz Trzciński +1
Exemplar-Free Class Incremental Learning (EFCIL) tackles the problem of training a model on a sequence of tasks without access to past data. Existing state-of-the-art methods repre…
MagMax: Leveraging Model Merging for Seamless Continual Learning
Daniel Marczak, Bartłomiej Twardowski, Tomasz Trzciński +1
This paper introduces a continual learning approach named MagMax, which utilizes model merging to enable large pre-trained models to continuously learn from new data without forget…
Realistic Evaluation of Test-Time Adaptation Algorithms: Unsupervised Hyperparameter Selection
Sebastian Cygert, Damian Sójka, Tomasz Trzciński +1
Test-Time Adaptation (TTA) has recently emerged as a promising strategy for tackling the problem of machine learning model robustness under distribution shifts by adapting the mode…
Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers
Filip Szatkowski, Yaoyue Zheng, Fei Yang +3
Continual learning is crucial for applying machine learning in challenging, dynamic, and often resource-constrained environments. However, catastrophic forgetting - overwriting pre…
GUIDE: Guidance-based Incremental Learning with Diffusion Models
Bartosz Cywiński, Kamil Deja, Tomasz Trzciński +2
We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten. Existing generative strategies combat catastro…
Divide and not forget: Ensemble of selectively trained experts in Continual Learning
Grzegorz Rypeść, Sebastian Cygert, Valeriya Khan +3
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 mixtu…
Revisiting Supervision for Continual Representation Learning
Daniel Marczak, Sebastian Cygert, Tomasz Trzciński +1
In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, there is a growing in…
Bayesian Flow Networks in Continual Learning
Mateusz Pyla, Kamil Deja, Bartłomiej Twardowski +1
Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. The…
Looking through the past: better knowledge retention for generative replay in continual learning
Valeriya Khan, Sebastian Cygert, Kamil Deja +2
In this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Current generative rehearsal methods are usually benchmarke…
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…
Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery
Grzegorz Rypeść, Daniel Marczak, Sebastian Cygert +2
Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend…
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
Filip Szatkowski, Mateusz Pyla, Marcin Przewięźlikowski +3
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 me…
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…
On the importance of cross-task features for class-incremental learning
Albin Soutif--Cormerais, Marc Masana, Joost van de Weijer +1
In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constrain…
Class-incremental learning: survey and performance evaluation on image classification
Marc Masana, Xialei Liu, Bartlomiej Twardowski +3
For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new…