no code implementations • 11 Mar 2018 • Bartosz Zieliński, Łukasz Struski, Marek Śmieja, Jacek Tabor
For this purpose, we train context encoder for 64x64 pixels images in a standard way and use its resized output to fill in the missing input region of the 128x128 context encoder, both in training and evaluation phase.
no code implementations • 22 Jan 2016 • Matthias Zeppelzauer, Bartosz Zieliński, Mateusz Juda, Markus Seidl
We investigate topological descriptors for 3D surface analysis, i. e. the classification of surfaces according to their geometric fine structure.
no code implementations • 22 Jun 2019 • Bartosz Zieliński, Agnieszka Sroka-Oleksiak, Dawid Rymarczyk, Adam Piekarczyk, Monika Brzychczy-Włoch
Diagnosis of fungal infections can rely on microscopic examination, however, in many cases, it does not allow unambiguous identification of the species due to their visual similarity.
no code implementations • MIDL 2019 • Bartosz Zieliński, Agnieszka Sroka-Oleksiak, Dawid Rymarczyk, Adam Piekarczyk, Monika Brzychczy-Włoch
Preliminary diagnosis of fungal infections can rely on microscopic examination.
no code implementations • 25 May 2020 • Dawid Rymarczyk, Adriana Borowa, Jacek Tabor, Bartosz Zieliński
There have been several attempts to create a model working with a bag of instances, however, they are assuming that there are no dependencies within the bag and the label is connected to at least one instance.
no code implementations • 2 Dec 2020 • Adriana Borowa, Dawid Rymarczyk, Dorota Ochońska, Monika Brzychczy-Włoch, Bartosz Zieliński
In this work, we analyze if it is possible to distinguish between different clones of the same bacteria species (Klebsiella pneumoniae) based only on microscopic images.
1 code implementation • 21 Jun 2021 • Witold Oleszkiewicz, Dominika Basaj, Igor Sieradzki, Michał Górszczak, Barbara Rychalska, Koryna Lewandowska, Tomasz Trzciński, Bartosz Zieliński
Motivated by this observation, we introduce a novel visual probing framework for explaining the self-supervised models by leveraging probing tasks employed previously in natural language processing.
no code implementations • 28 Jul 2021 • Łukasz Struski, Tomasz Danel, Marek Śmieja, Jacek Tabor, Bartosz Zieliński
Recent years have seen a surge in research on deep interpretable neural networks with decision trees as one of the most commonly incorporated tools.
no code implementations • 21 Aug 2022 • Łukasz Struski, Jacek Tabor, Bartosz Zieliński
Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true.
1 code implementation • 11 Mar 2023 • Adam Pardyl, Grzegorz Rypeść, Grzegorz Kurzejamski, Bartosz Zieliński, Tomasz Trzciński
Active visual exploration addresses the issue of limited sensor capabilities in real-world scenarios, where successive observations are actively chosen based on the environment.
no code implementations • 18 Jun 2023 • Łukasz Struski, Dawid Rymarczyk, Arkadiusz Lewicki, Robert Sabiniewicz, Jacek Tabor, Bartosz Zieliński
The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label.
no code implementations • 16 Aug 2023 • Mikołaj Sacha, Bartosz Jura, Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński
Prototypical parts-based networks are becoming increasingly popular due to their faithful self-explanations.
no code implementations • 23 Sep 2023 • Adam Pardyl, Grzegorz Kurzejamski, Jan Olszewski, Tomasz Trzciński, Bartosz Zieliński
Vision transformers have excelled in various computer vision tasks but mostly rely on rigid input sampling using a fixed-size grid of patches.
no code implementations • 26 Nov 2023 • Jan Olszewski, Dawid Rymarczyk, Piotr Wójcik, Mateusz Pach, Bartosz Zieliński
To reduce this computational inefficiency, we introduce the TOken REcycling (TORE) modification for the ViT inference, which can be used with any architecture.
1 code implementation • 12 Mar 2024 • Łukasz Struski, Adam Pardyl, Jacek Tabor, Bartosz Zieliński
Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true.
1 code implementation • 4 Apr 2024 • Adam Pardyl, Michał Wronka, Maciej Wołczyk, Kamil Adamczewski, Tomasz Trzciński, Bartosz Zieliński
Active Visual Exploration (AVE) is a task that involves dynamically selecting observations (glimpses), which is critical to facilitate comprehension and navigation within an environment.
1 code implementation • ICCV 2023 • Dawid Rymarczyk, Joost Van de Weijer, Bartosz Zieliński, Bartłomiej Twardowski
Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks.
1 code implementation • 31 May 2023 • Marcin Przewięźlikowski, Mateusz Pyla, Bartosz Zieliński, Bartłomiej Twardowski, Jacek Tabor, Marek Śmieja
By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo are able to reach quality on par with supervised approaches.
1 code implementation • 21 Dec 2018 • Bartosz Zieliński, Michał Lipiński, Mateusz Juda, Matthias Zeppelzauer, Paweł Dłotko
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs).
1 code implementation • 24 Aug 2021 • Dawid Rymarczyk, Adam Pardyl, Jarosław Kraus, Aneta Kaczyńska, Marek Skomorowski, Bartosz Zieliński
Multiple Instance Learning (MIL) gains popularity in many real-life machine learning applications due to its weakly supervised nature.
1 code implementation • 28 Jan 2023 • Mikołaj Sacha, Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński
We introduce ProtoSeg, a novel model for interpretable semantic image segmentation, which constructs its predictions using similar patches from the training set.
1 code implementation • 29 Nov 2020 • Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński
In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions.
1 code implementation • 6 Dec 2021 • Dawid Rymarczyk, Łukasz Struski, Michał Górszczak, Koryna Lewandowska, Jacek Tabor, Bartosz Zieliński
We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes.
1 code implementation • 18 Jan 2024 • Grzegorz Rypeść, Sebastian Cygert, Valeriya Khan, Tomasz Trzciński, Bartosz Zieliński, Bartłomiej Twardowski
Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know.
1 code implementation • NeurIPS 2018 • Marek Smieja, Łukasz Struski, Jacek Tabor, Bartosz Zieliński, Przemysław Spurek
We propose a general, theoretically justified mechanism for processing missing data by neural networks.