Search Results for author: Łukasz Struski

Found 15 papers, 6 papers with code

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

Processing of incomplete images by (graph) convolutional neural networks

no code implementations26 Oct 2020 Tomasz Danel, Marek Śmieja, Łukasz Struski, Przemysław Spurek, Łukasz Maziarka

We investigate the problem of training neural networks from incomplete images without replacing missing values.


Flow-based Anomaly Detection

no code implementations6 Oct 2020 Łukasz Maziarka, Marek Śmieja, Marcin Sendera, Łukasz Struski, Jacek Tabor, Przemysław Spurek

We propose OneFlow - a flow-based one-class classifier for anomaly (outliers) detection that finds a minimal volume bounding region.

Anomaly Detection One-class classifier

Estimating conditional density of missing values using deep Gaussian mixture model

1 code implementation5 Oct 2020 Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski

We consider the problem of estimating the conditional probability distribution of missing values given the observed ones.


LocoGAN -- Locally Convolutional GAN

1 code implementation18 Feb 2020 Łukasz Struski, Szymon Knop, Jacek Tabor, Wiktor Daniec, Przemysław Spurek

In the paper we construct a fully convolutional GAN model: LocoGAN, which latent space is given by noise-like images of possibly different resolutions.

A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints

no code implementations18 Jan 2020 Marek Śmieja, Łukasz Struski, Mário A. T. Figueiredo

In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints.

General Classification

Spatial Graph Convolutional Networks

1 code implementation11 Sep 2019 Tomasz Danel, Przemysław Spurek, Jacek Tabor, Marek Śmieja, Łukasz Struski, Agnieszka Słowik, Łukasz Maziarka

Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds.

Image Classification

Realism Index: Interpolation in Generative Models With Arbitrary Prior

no code implementations6 Apr 2019 Łukasz Struski, Jacek Tabor, Igor Podolak, Aleksandra Nowak, Krzysztof Maziarz

In order to perform plausible interpolations in the latent space of a generative model, we need a measure that credibly reflects if a point in an interpolation is close to the data manifold being modelled, i. e. if it is convincing.

Set Aggregation Network as a Trainable Pooling Layer

1 code implementation3 Oct 2018 Łukasz Maziarka, Marek Śmieja, Aleksandra Nowak, Jacek Tabor, Łukasz Struski, Przemysław Spurek

Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data.

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.

Semi-supervised model-based clustering with controlled clusters leakage

no code implementations4 May 2017 Marek Śmieja, Łukasz Struski, Jacek Tabor

In this paper, we focus on finding clusters in partially categorized data sets.

Pointed subspace approach to incomplete data

no code implementations2 May 2017 Łukasz Struski, Marek Śmieja, Jacek Tabor

Incomplete data are often represented as vectors with filled missing attributes joined with flag vectors indicating missing components.

Dimensionality Reduction General Classification

Generalized RBF kernel for incomplete data

no code implementations5 Dec 2016 Łukasz Struski, Marek Śmieja, Jacek Tabor

We construct $\bf genRBF$ kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data.

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