Search Results for author: Bartosz Zieliński

Found 11 papers, 3 papers with code

SONG: Self-Organizing Neural Graphs

no code implementations28 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.

Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations

1 code implementation21 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.

Representation Learning

Classifying bacteria clones using attention-based deep multiple instance learning interpreted by persistence homology

no code implementations2 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.

Multiple Instance Learning

ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery

no code implementations29 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.

General Classification Image Classification

Kernel Self-Attention in Deep Multiple Instance Learning

no code implementations25 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.

Multiple Instance Learning whole slide images

Deep learning approach to description and classification of fungi microscopic images

no code implementations22 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.

General Classification

Persistence Bag-of-Words for Topological Data Analysis

1 code implementation21 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).

Topological Data Analysis

Processing of missing data by neural networks

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.


Cascade context encoder for improved inpainting

no code implementations11 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.

Topological descriptors for 3D surface analysis

no code implementations22 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.

General Classification

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