Bartłomiej Twardowski

1.4k total citations · 1 hit paper
21 papers, 733 citations indexed

About

Bartłomiej Twardowski is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Bartłomiej Twardowski has authored 21 papers receiving a total of 733 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 12 papers in Computer Vision and Pattern Recognition and 3 papers in Information Systems. Recurrent topics in Bartłomiej Twardowski's work include Domain Adaptation and Few-Shot Learning (14 papers), Multimodal Machine Learning Applications (9 papers) and Machine Learning and ELM (6 papers). Bartłomiej Twardowski is often cited by papers focused on Domain Adaptation and Few-Shot Learning (14 papers), Multimodal Machine Learning Applications (9 papers) and Machine Learning and ELM (6 papers). Bartłomiej Twardowski collaborates with scholars based in Poland, Spain and China. Bartłomiej Twardowski's co-authors include Joost van de Weijer, Xialei Liu, Andrew D. Bagdanov, Marc Masana, Kai Wang, Shangling Jui, Yongmei Cheng, Lu Yu, Luis Herranz and Yu Lu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and Knowledge-Based Systems.

In The Last Decade

Bartłomiej Twardowski

20 papers receiving 711 citations

Hit Papers

Class-Incremental Learning: Survey and Performance Evalua... 2022 2026 2023 2024 2022 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Class-Incremental Learning: Survey and Performance Evaluation on Image Classification
    2022 342 citations Marc Masana, Xialei Liu et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Bartłomiej Twardowski Poland 8 542 304 108 57 47 21 733
Faliang Huang China 13 396 0.7× 152 0.5× 133 1.2× 58 1.0× 24 0.5× 50 702
Ritam Guha India 12 404 0.7× 164 0.5× 67 0.6× 46 0.8× 28 0.6× 24 594
Zhixiang Xu China 10 457 0.8× 245 0.8× 52 0.5× 35 0.6× 25 0.5× 30 693
Ghada Khoriba Egypt 9 382 0.7× 146 0.5× 61 0.6× 68 1.2× 38 0.8× 42 608
Misha Denil United Kingdom 8 460 0.8× 329 1.1× 56 0.5× 33 0.6× 13 0.3× 13 731
Lili Zhao China 13 249 0.5× 283 0.9× 162 1.5× 48 0.8× 17 0.4× 75 684
Shuangfei Zhai United States 11 310 0.6× 279 0.9× 66 0.6× 24 0.4× 20 0.4× 16 577
Kuo-Kun Tseng China 14 269 0.5× 243 0.8× 40 0.4× 53 0.9× 22 0.5× 49 737

Countries citing papers authored by Bartłomiej Twardowski

Since Specialization
Citations

This map shows the geographic impact of Bartłomiej Twardowski's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bartłomiej Twardowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bartłomiej Twardowski more than expected).

Fields of papers citing papers by Bartłomiej Twardowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bartłomiej Twardowski. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bartłomiej Twardowski. The network helps show where Bartłomiej Twardowski may publish in the future.

Co-authorship network of co-authors of Bartłomiej Twardowski

This figure shows the co-authorship network connecting the top 25 collaborators of Bartłomiej Twardowski. A scholar is included among the top collaborators of Bartłomiej Twardowski based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bartłomiej Twardowski. Bartłomiej Twardowski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wang, Kai, et al.. (2025). The Art of Deception: Color Visual Illusions and Diffusion Models. 18642–18652. 1 indexed citations
2.
Twardowski, Bartłomiej, et al.. (2024). Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning. 28525–28534. 4 indexed citations
3.
4.
Zieliński, Bartosz, et al.. (2024). Augmentation-aware self-supervised learning with conditioned projector. Knowledge-Based Systems. 305. 112572–112572. 2 indexed citations
5.
Twardowski, Bartłomiej, et al.. (2024). Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning. 1679–1689. 4 indexed citations
6.
Twardowski, Bartłomiej, et al.. (2024). Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning. Jagiellonian University Repository (Jagiellonian University). 1966–1976. 2 indexed citations
7.
Twardowski, Bartłomiej, et al.. (2024). Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers. 4075–4084. 1 indexed citations
8.
Deja, Kamil Rafał, et al.. (2024). Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning. IEEE Access. 12. 45309–45317. 1 indexed citations
9.
Twardowski, Bartłomiej, et al.. (2023). Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning. 3504–3509. 3 indexed citations
10.
Twardowski, Bartłomiej, et al.. (2023). AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation. 3483–3487. 2 indexed citations
11.
Twardowski, Bartłomiej, et al.. (2023). Looking through the past: better knowledge retention for generative replay in continual learning. 3488–3492. 5 indexed citations
12.
Rymarczyk, Dawid, Joost van de Weijer, Bartosz Zieliński, & Bartłomiej Twardowski. (2023). ICICLE: Interpretable Class Incremental Continual Learning. Jagiellonian University Repository (Jagiellonian University). 1887–1898. 14 indexed citations
13.
Twardowski, Bartłomiej, et al.. (2022). Continually Learning Self-Supervised Representations with Projected Functional Regularization. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 3866–3876. 18 indexed citations
14.
Masana, Marc, et al.. (2020). Class-incremental learning: survey and performance evaluation. arXiv (Cornell University). 46 indexed citations
15.
Yu, Lu, Bartłomiej Twardowski, Xialei Liu, et al.. (2020). Semantic Drift Compensation for Class-Incremental Learning. 6980–6989. 183 indexed citations
16.
Twardowski, Bartłomiej, et al.. (2019). Widget detection on screenshots using computer vision and machine learning algorithms. 11. 28–28. 1 indexed citations
17.
Twardowski, Bartłomiej, et al.. (2019). Deep Learning Optimization Tasks and Metaheuristic Methods. Fundamenta Informaticae. 168(2-4). 185–218. 1 indexed citations
18.
Twardowski, Bartłomiej. (2016). Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks. 273–276. 63 indexed citations
19.
Twardowski, Bartłomiej, et al.. (2015). IoT and Context-Aware Mobile Recommendations Using Multi-agent Systems. 33–40. 15 indexed citations
20.
Twardowski, Bartłomiej, et al.. (2014). Multi-agent Architecture for Real-Time Big Data Processing. 333–337. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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