2 citations · 4 across the 4 of their papers we have counts for
13 papers
MisConv: Convolutional Neural Networks for Missing Data
Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski +1
Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image…
Flow-based SVDD for anomaly detection
Marcin Sendera, Marek Śmieja, Łukasz Maziarka +3
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other appro…
HyperPocket: Generative Point Cloud Completion
Przemysław Spurek, Artur Kasymov, Marcin Mazur +5
Scanning real-life scenes with modern registration devices typically give incomplete point cloud representations, mostly due to the limitations of the scanning process and 3D occlu…
Processing of incomplete images by (graph) convolutional neural networks
Tomasz Danel, Marek Śmieja, Łukasz Struski +2
We investigate the problem of training neural networks from incomplete images without replacing missing values. For this purpose, we first represent an image as a graph, in which m…
Estimating conditional density of missing values using deep Gaussian mixture model
Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski
We consider the problem of estimating the conditional probability distribution of missing values given the observed ones. We propose an approach, which combines the flexibility of…
A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints
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. In contrast to existing approaches…