Publications (6)
A Wiener Process Perspective on Local Intrinsic Dimension Estimation Methods
Piotr Tempczyk, Åukasz Garncarek, Dominik Filipiak +1
Local intrinsic dimension (LID) estimation methods have received a lot of attention in recent years thanks to the progress in deep neural networks and generative modeling. In oppos…
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
Piotr Tempczyk, RafaÅ Michaluk, Åukasz Garncarek +3
Most of the existing methods for estimating the local intrinsic dimension of a data distribution do not scale well to high-dimensional data. Many of them rely on a non-parametric n…
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
One Simple Trick to Fix Your Bayesian Neural Network
Piotr Tempczyk, Ksawery SmoczyÅski, Philip Smolenski-Jensen +1
One of the most popular estimation methods in Bayesian neural networks (BNN) is mean-field variational inference (MFVI). In this work, we show that neural networks with ReLU activa…
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation
Dominik Filipiak, Piotr Tempczyk, Marek Cygan
We present n-CPS - a generalisation of the recent state-of-the-art cross pseudo supervision (CPS) approach for the task of semi-supervised semantic segmentation. In n-CPS, there ar…
Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding
Dominik Filipiak, Andrzej ZapaÅa, Piotr Tempczyk +2
We present Polite Teacher, a simple yet effective method for the task of semi-supervised instance segmentation. The proposed architecture relies on the Teacher-Student mutual learn…