153 citations · 226 across the 6 of their papers we have counts for
7 papers
Self-supervised Visualisation of Medical Image Datasets
Ifeoma Veronica Nwabufo, Jan Niklas Böhm, Philipp Berens +1
Self-supervised learning methods based on data augmentations, such as SimCLR, BYOL, or DINO, allow obtaining semantically meaningful representations of image datasets and are widel…
Unsupervised visualization of image datasets using contrastive learning
Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningf…
From -SNE to UMAP with contrastive learning
Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht +1
Neighbor embedding methods -SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. Motivated from entirely different viewpoints, their loss functions…
Curiosity-driven 3D Object Detection Without Labels
David Griffiths, Jan Boehm, Tobias Ritschel
In this paper we set out to solve the task of 6-DOF 3D object detection from 2D images, where the only supervision is a geometric representation of the objects we aim to find. In d…
SynthCity: A large scale synthetic point cloud
David Griffiths, Jan Boehm
With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training da…
A review on deep learning techniques for 3D sensed data classification
David Griffiths, Jan Boehm
Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point c…