Publications

Jan Dubiński, Antoni Kowalczuk, Stanisław Pawlak, Przemysław Rokita, Tomasz Trzciński, Paweł Morawiecki (2024). Towards More Realistic Membership Inference Attacks on Large Diffusion Models. WACV 2024.

Jan Dubiński, Stanisław Pawlak, Franziska Boenisch, Tomasz Trzciński, Adam Dziedzic (2023). Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. NeurIPS 2023.

Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi, Virginia Estellers, Kwang Moo Yi, Julien Valentin, Tomasz Trzciński, Marek Kowalski (2023). BlendFields: Few-Shot Example-Driven Facial Modeling. CVPR 2023.

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Kamil Deja, Tomasz Trzciński, Jakub M. Tomczak (2023). Learning Data Representations with Joint Diffusion Models. ECML 2023.

Kamil Deja, Anna Kuzina, Tomasz Trzciński, Jakub M. Tomczak (2022). On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. NeurIPS 2022.

Jan Dubiński, Kamil Deja, Sandro Wenzel, Przemysław Rokita, Tomasz Trzciński (2022). Selectively increasing the diversity of GAN-generated samples. ICONIP 2022.

Kamil Deja, pawel-wawrzynski, Wojciech Masarczyk, Daniel Marczak, Tomasz Trzciński (2022). Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning. IJCAI 2022.

Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciński, Andrea Tagliasacchi (2022). CoNeRF: Controllable Neural Radiance Fields. CVPR 2022.

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Marcin Sendera, Jacek Tabor, Aleksandra Nowak, Andrzej Bedychaj, Massimiliano Patacchiola, Tomasz Trzciński, Przemysław Spurek, Maciej Zięba (2021). Non-Gaussian Gaussian Processes for Few-Shot Regression. NeurIPS 2021.

Przemysław Spurek, Maciej Zięba, Jacek Tabor, Tomasz Trzciński (2021). General hypernetwork framework for creating 3D point clouds. TPAMI.

Maciej Wołczyk, Bartosz Wójcik, Klaudia Bałazy, Igor Podolak, Jacek Tabor, Marek Śmieja, Tomasz Trzciński (2021). Zero Time Waste: Recycling Predictions in Early Exit Neural Networks. NeurIPS 2021.

Mateusz Ostaszewski, Lea M. Trenkwalder, Wojciech Masarczyk, Eleanor Scerri, Vedran Dunjko (2021). Reinforcement learning for optimization of variational quantum circuit architectures. NeurIPS 2021.

Wojciech Masarczyk, Kamil Deja, Tomasz Trzciński (2021). On robustness of generative representations against catastrophic forgetting. ICONIP 2021.

Tomasz Włodarczyk, Adam Klasa, Michał Lipa, Arkadiusz Sitek, Tomasz Trzciński (2021). FetalNet: Multi-task deep learning framework for fetal ultrasound biometric measurements. ICONIP 2021.

Witold Oleszkiewicz, Dominika Basaj, Igor Sieradzki, Michał Górszczak, Barbara Rychalska, Tomasz Trzciński, Bartosz Zieliński (2021). Explaining Self-Supervised Image Representations with Visual Probing. IJCAI-21.

Jacek Komorowski, Monika Wysoczańska, Tomasz Trzciński (2021). MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition. IJCNN-21.

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Kamil Deja, Jan Dubiński, Piotr Nowak, Sandro Wenzel, Przemysław Spurek, Tomasz Trzciński (2020). End-to-End Sinkhorn Autoencoder With Noise Generator. IEEE Access, vol. 9.

Tomasz Włodarczyk, Tomasz Szczepański, Przemysław Rokita, Nicole Sochacki-Wójcicka, Jakub Wójcicki, Michał Lipa, Tomasz Trzciński (2020). Machine Learning Methods for Preterm Birth Prediction: A Review. MDPI Electronics.

Kamil Deja, Daniel Marczak, Wojciech Masarczyk, Tomasz Trzciński (2020). BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning. IJCNN-21.

Jacek Komorowski (2020). MinkLoc3D: Point Cloud Based Large-Scale Place Recognition. WACV 2021.

Tomasz Włodarczyk, Tomasz Trzciński, Przemysław Rokita, Nicole Sochacki-Wójcicka, Michał Lipa, Jakub Wójcicki (2020). Spontaneous Preterm Birth Prediction Using Convolutional Neural Networks. PIPPI 2020.

Kacper Kania, Maciej Zięba, Tomasz Kajdanowicz (2020). UCSG-Net - Unsupervised Discovering of Constructive Solid Geometry Tree. NeurIPS 2020.

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Maciej Zamorski, Maciej Zięba, Piotr Klukowski, Karol Kurach, Wojciech Stokowiec, Tomasz Trzciński (2020). Adversarial autoencoders for compact representations of 3D point clouds. Computer Vision and Image Understanding.

Witold Oleszkiewicz, Taro Makino, Stanisław Jastrzębski, Tomasz Trzciński, Linda Moy, Kyunghyun Cho, Laura Heacock, Krzysztof Geras (2020). Understanding the robustness of deep neural network classifiers for breast cancer screening. ICRL Workshop 2020.

Michał Koperski, Tomasz Konopczyński, Rafał Nowak, Piotr Semberecki, Tomasz Trzciński (2020). Plugin Networks for Inference under Partial Evidence. WACV 2020.

Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zięba, Tomasz Trzciński (2020). Hypernetwork approach to generating point clouds. ICML 2020.

Grzegorz Kurzejamski, Jacek Komorowski, Łukasz Dabala, Konrad Czarnota, Simon Lynen, Tomasz Trzciński (2020). SuperNCN: Neighbourhood consensus network for robust outdoor scenes matching. ACIVS 2020.

Tomasz Włodarczyk, Tomasz Trzciński, Przemysław Rokita, Nicole Sochacki-Wójcicka, Michał Lipa, Jakub Wójcicki (2019). Estimation of Preterm Birth Markers with U-Net Segmentation Network. PIPPI 2019.

Maciej Pęśko, Adam Svystun, Paweł Andruszkiewicz, Przemysław Rokita, Tomasz Trzciński (2019). Comixify: Transform video into a comics. Fundamenta Informaticae, vol. 168.

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Ivona Tautkute, Tomasz Trzciński (2019). Classifying and Visualizing Emotions with EmotionalDAN. Fundamenta Informaticae, vol. 168.

Ivona Tautkute, Tomasz Trzciński, Aleksander Skorupa, Łukasz Brocki, Krzysztof Marasek (2019). DeepStyle: Multimodal Search Engine for Fashion and Interior Design. IEEE Access, vol. 7.

Marek Komorowski, Tomasz Trzciński (2019). Random Binary Search Trees for approximate nearest neighbour search in binary spaces. Applied Soft Computing, vol. 79.

Tomasz Trzciński, Łukasz Graczykowski, Michał Glinka, for the ALICE Collaboration (2019). Using Random Forest Classifier for particle identification in the ALICE Experiment. ITSRCP 2018.

Kamil Deja, Tomasz Trzciński, Łukasz Graczykowski, for the ALICE Collaboration (2019). Generative models for fast cluster simulations in the TPC for the ALICE experiment. ITSRCP 2018.

Adam Bielski, Tomasz Trzciński (2018). Understanding Multimodal Popularity Prediction of Social Media Videos With Self-Attention. IEEE Access, vol. 6.

Maciej Zięba, Piotr Semberecki, Tarek El-Gaaly, Tomasz Trzciński (2018). BinGAN: Learning Compact Binary Descriptors with a Regularized GAN. NeurIPS 2018.

Witold Oleszkiewicz, Peter Kairouz, Karol Piczak, Ram Rajagopal, Tomasz Trzciński (2018). Siamese generative adversarial privatizer for biometric data. ACCV 2018.

Norbert Kapiński, Jakub Zieliński, Bartosz Borucki, Tomasz Trzciński, Beata Ciszkowska-Lyson, Krzysztof Nowiński (2018). Estimating Achilles tendon healing progress with convolutional neural networks. MICCAI 2018.

Tomasz Trzciński, Jacek Komorowski, Łukasz Dabala, Konrad Czarnota, Grzegorz Kurzejamski, Simon Lynen (2018). SConE: Siamese Constellation Embedding Descriptor for Image Matching. ECCV Workshop 2018.

Jacek Komorowski, Konrad Czarnota, Tomasz Trzciński, Łukasz Dabala, Simon Lynen (2018). Interest point detectors stability evaluation on ApolloScape dataset. ECCV Workshop 2018.

Adam Bielski, Tomasz Trzciński (2018). Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism. CVPR Workshop 2018.

Ivona Tautkute, Tomasz Trzciński, Adam Bielski (2018). I know how you feel: Emotion recognition with facial landmarks. CVPR Workshop 2018.

Tomasz Trzciński, Adam Bielski, Paweł Cyrta, Matthew Żak (2017). SocialML: machine learning for social media video creators. NIPS Workshop 2018.

Marek Kowalski, Jacek Naruniec, Tomasz Trzciński (2017). Deep Alignment Network: A convolutional neural network for robust face alignment. CVPR Workshop 2017.

Tomasz Trzciński, Przemysław Rokita (2017). Predicting Popularity of Online Videos Using Support Vector Regression. IEEE Transactions on Multimedia, vol. 19, issue 11.

Jacek Komorowski, Monika Wysoczańska, Tomasz Trzciński (0001). EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale. IEEE Robotics and Automation Letters.