10 citations · 14 across the 5 of their papers we have counts for
6 papers
A Wiener Process Perspective on Local Intrinsic Dimension Estimation Methods
Piotr Tempczyk, Łukasz 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…
Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding
Dominik Filipiak, Andrzej Zapała, 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…
One Simple Trick to Fix Your Bayesian Neural Network
Piotr Tempczyk, Ksawery Smoczyński, 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…
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
Piotr Tempczyk, Rafał Michaluk, Łukasz 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…
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…