Michael Kampffmeyer

research

∙ 07/02/2023

Defending Against Malicious Behaviors in Federated Learning with Blockchain

In the era of deep learning, federated learning (FL) presents a promisin...

0 Nanqing Dong, et al. ∙

research

∙ 06/19/2023

Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation

In prediction of forest parameters with data from remote sensing (RS), r...

0 Sara Björk, et al. ∙

research

∙ 03/02/2023

Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation

Precise ischemic lesion segmentation plays an essential role in improvin...

0 Luca Tomasetti, et al. ∙

research

∙ 11/25/2022

Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning

In this paper, we target image-based person-to-person virtual try-on in ...

0 Zaiyu Huang, et al. ∙

research

∙ 10/15/2022

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

The need for interpretable models has fostered the development of self-e...

0 Srishti Gautam, et al. ∙

research

∙ 07/27/2022

PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on

Image-based virtual try-on is one of the most promising applications of ...

6 Zhenyu Xie, et al. ∙

research

∙ 06/30/2022

Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

Using decentralized data for federated training is one promising emergin...

0 Nanqing Dong, et al. ∙

research

∙ 05/18/2022

The Kernelized Taylor Diagram

This paper presents the kernelized Taylor diagram, a graphical framework...

4 Kristoffer Wickstrøm, et al. ∙

research

∙ 03/30/2022

Automatic Identification of Chemical Moieties

In recent years, the prediction of quantum mechanical observables with m...

18 Jonas Lederer, et al. ∙

research

∙ 03/17/2022

Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series

The lack of labeled data is a key challenge for learning useful represen...

0 Kristoffer Wickstrøm, et al. ∙

research

∙ 03/03/2022

Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels

Recent work has shown that label-efficient few-shot learning through sel...

0 Stine Hansen, et al. ∙

research

∙ 01/10/2022

Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation

The recent trend of integrating multi-source Chest X-Ray datasets to imp...

0 Srishti Gautam, et al. ∙

research

∙ 11/20/2021

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN

Image-based virtual try-on is one of the most promising applications of ...

1 Zhenyu Xie, et al. ∙

research

∙ 11/06/2021

Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks

Multi-modality data is becoming readily available in remote sensing (RS)...

10 Qinghui Liu, et al. ∙

research

∙ 10/09/2021

Discriminative Multimodal Learning via Conditional Priors in Generative Models

Deep generative models with latent variables have been used lately to le...

0 Rogelio A Mancisidor, et al. ∙

research

∙ 08/27/2021

This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation

Current machine learning models have shown high efficiency in solving a ...

0 Srishti Gautam, et al. ∙

research

∙ 05/20/2021

Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification

Entanglement is a physical phenomenon, which has fueled recent successes...

23 Nanqing Dong, et al. ∙

research

∙ 03/13/2021

Reconsidering Representation Alignment for Multi-view Clustering

Aligning distributions of view representations is a core component of to...

0 Daniel J. Trosten, et al. ∙

research

∙ 12/07/2020

Joint Optimization of an Autoencoder for Clustering and Embedding

Incorporating k-means-like clustering techniques into (deep) autoencoder...

0 Ahcène Boubekki, et al. ∙

research

∙ 11/28/2020

Towards Robust Medical Image Segmentation on Small-Scale Data with Incomplete Labels

The data-driven nature of deep learning models for semantic segmentation...

15 Nanqing Dong, et al. ∙

research

∙ 09/03/2020

SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation

Capturing global contextual representations by exploiting long-range pix...

11 Qinghui Liu, et al. ∙

research

∙ 04/21/2020

Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation

We propose a novel architecture called the Multi-view Self-Constructing ...

0 Qinghui Liu, et al. ∙

research

∙ 04/15/2020

Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images

Image translation with convolutional autoencoders has recently been used...

8 Luigi T. Luppino, et al. ∙

research

∙ 03/15/2020

Self-Constructing Graph Convolutional Networks for Semantic Labeling

Graph Neural Networks (GNNs) have received increasing attention in many ...

0 Qinghui Liu, et al. ∙

research

∙ 03/09/2020

Dense Dilated Convolutions Merging Network for Land Cover Classification

Land cover classification of remote sensing images is a challenging task...

0 Qinghui Liu, et al. ∙

research

∙ 01/13/2020

Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

Image translation with convolutional neural networks has recently been u...

25 Luigi Tommaso Luppino, et al. ∙

research

∙ 09/25/2019

Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels

Analyzing deep neural networks (DNNs) via information plane (IP) theory ...

33 Kristoffer Wickstrøm, et al. ∙

research

∙ 09/07/2019

Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network

It is important, but challenging, for the forest industry to accurately ...

0 Qinghui Liu, et al. ∙

research

∙ 08/30/2019

Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images

We propose a network for semantic mapping called the Dense Dilated Convo...

0 Qinghui Liu, et al. ∙

research

∙ 03/14/2019

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Learning data representations that reflect the customers' creditworthine...

0 Rogelio A Mancisidor, et al. ∙

research

∙ 02/13/2019

Deep Divergence-Based Approach to Clustering

A promising direction in deep learning research consists in learning rep...

36 Michael Kampffmeyer, et al. ∙

research

∙ 11/29/2018

Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series

The task of clustering unlabeled time series and sequences entails a par...

0 Daniel J. Trosten, et al. ∙

research

∙ 07/29/2018

Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images

Convolutional neural networks have led to significant breakthroughs in t...

0 Nanqing Dong, et al. ∙

research

∙ 07/19/2018

The Deep Kernelized Autoencoder

Autoencoders learn data representations (codes) in such a way that the i...

0 Michael Kampffmeyer, et al. ∙

research

∙ 07/17/2018

Query-Conditioned Three-Player Adversarial Network for Video Summarization

Video summarization plays an important role in video understanding by se...

0 Yujia Zhang, et al. ∙

research

∙ 07/16/2018

Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps

Convolutional Neural Networks (CNNs) are propelling advances in a range ...

0 Kristoffer Wickstrøm, et al. ∙

research

∙ 07/10/2018

Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful

Raven's Progressive Matrices are one of the widely used tests in evaluat...

4 Rajesh Chidambaram, et al. ∙

research

∙ 07/10/2018

Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio

The cardiothoracic ratio (CTR), a clinical metric of heart size in chest...

2 Nanqing Dong, et al. ∙

research

∙ 06/07/2018

Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder

Identifying customer segments in retail banking portfolios with differen...

0 Rogelio Andrade Mancisidor, et al. ∙

research

∙ 05/29/2018

Rethinking Knowledge Graph Propagation for Zero-Shot Learning

The potential of graph convolutional neural networks for the task of zer...

0 Michael Kampffmeyer, et al. ∙

research

∙ 05/09/2018

Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders

Learning compressed representations of multivariate time series (MTS) fa...

0 Filippo Maria Bianchi, et al. ∙

research

∙ 04/30/2018

DTR-GAN: Dilated Temporal Relational Adversarial Network for Video Summarization

The large amount of videos popping up every day, make it is more and mor...

0 Yujia Zhang, et al. ∙

research

∙ 04/20/2018

ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation

Salient segmentation aims to segment out attention-grabbing regions, a c...

0 Michael Kampffmeyer, et al. ∙

research

∙ 11/17/2017

Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks

Clinical measurements that can be represented as time series constitute ...

0 Andreas Storvik Strauman, et al. ∙

research

∙ 09/21/2017

Urban Land Cover Classification with Missing Data Using Deep Convolutional Neural Networks

Automatic urban land cover classification is a classical problem in remo...

0 Michael Kampffmeyer, et al. ∙

research

∙ 02/08/2017

Deep Kernelized Autoencoders

In this paper we introduce the deep kernelized autoencoder, a neural net...

0 Michael Kampffmeyer, et al. ∙

research

∙ 01/18/2017

Temporal Overdrive Recurrent Neural Network

In this work we present a novel recurrent neural network architecture de...

0 Filippo Maria Bianchi, et al. ∙

Michael Kampffmeyer

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