Search Results for author: Michael Kampffmeyer

Found 57 papers, 25 papers with code

Robust Classification by Coupling Data Mollification with Label Smoothing

no code implementations3 Jun 2024 Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone

Introducing training-time augmentations is a key technique to enhance generalization and prepare deep neural networks against test-time corruptions.

Data Augmentation Robust classification +1

MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation

no code implementations1 May 2024 Xujie Zhang, Ente Lin, Xiu Li, Yuxuan Luo, Michael Kampffmeyer, Xin Dong, Xiaodan Liang

Besides, to remove the segmentation dependency, MMTryon uses a parsing-free garment encoder and leverages a novel scalable data generation pipeline to convert existing VITON datasets to a form that allows MMTryon to be trained without requiring any explicit segmentation.

Segmentation Virtual Try-on

ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations

1 code implementation CVPR 2024 Rwiddhi Chakraborty, Adrian Sletten, Michael Kampffmeyer

Based on the insight that a trained model's classification strategies can be inferred accurately based on explainability heatmaps, we introduce ExMap, an unsupervised two stage mechanism designed to enhance group robustness in traditional classifiers.

Defending Against Poisoning Attacks in Federated Learning with Blockchain

no code implementations2 Jul 2023 Nanqing Dong, Zhipeng Wang, Jiahao Sun, Michael Kampffmeyer, William Knottenbelt, Eric Xing

In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy.

Federated Learning

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

1 code implementation19 Jun 2023 Sara Björk, Stian N. Anfinsen, Michael Kampffmeyer, Erik Næsset, Terje Gobakken, Lennart Noordermeer

These results are consistent for experiments on above-ground biomass prediction in Tanzania and stem volume prediction in Norway, representing a diversity in parameters and forest types that emphasises the robustness of the approach.

Imputation Parameter Prediction +1

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

1 code implementation2 Mar 2023 Luca Tomasetti, Stine Hansen, Mahdieh Khanmohammadi, Kjersti Engan, Liv Jorunn Høllesli, Kathinka Dæhli Kurz, Michael Kampffmeyer

Precise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest mortality rate.

Few-Shot Learning Ischemic Stroke Lesion Segmentation +2

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

1 code implementation25 Nov 2022 Zaiyu Huang, Hanhui Li, Zhenyu Xie, Michael Kampffmeyer, Qingling Cai, Xiaodan Liang

Existing methods are restricted in this setting as they estimate garment warping flows mainly based on 2D poses and appearance, which omits the geometric prior of the 3D human body shape.

Virtual Try-on

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

no code implementations27 Jul 2022 Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xin Dong, Feida Zhu, Xiaodan Liang

In this work, we take a step forwards to explore versatile virtual try-on solutions, which we argue should possess three main properties, namely, they should support unsupervised training, arbitrary garment categories, and controllable garment editing.

Disentanglement Image Generation +1

Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

no code implementations30 Jun 2022 Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu

In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes.

Federated Learning Object Recognition +2

The Kernelized Taylor Diagram

1 code implementation18 May 2022 Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen

Our proposed kernelized Taylor diagram is capable of visualizing similarities between populations with minimal assumptions of the data distributions.

Data Visualization

Automatic Identification of Chemical Moieties

no code implementations30 Mar 2022 Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke

In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.

Property Prediction

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

1 code implementation3 Mar 2022 Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer

Motivated by this, and the observation that the foreground class (e. g., one organ) is relatively homogeneous, we propose a novel anomaly detection-inspired approach to few-shot medical image segmentation in which we refrain from modeling the background explicitly.

Anomaly Detection Cardiac Segmentation +5

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

no code implementations10 Jan 2022 Srishti Gautam, Marina M. -C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer

The recent trend of integrating multi-source Chest X-Ray datasets to improve automated diagnostics raises concerns that models learn to exploit source-specific correlations to improve performance by recognizing the source domain of an image rather than the medical pathology.

Pneumonia Detection

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

1 code implementation NeurIPS 2021 Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xiaodan Liang

Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential.

Disentanglement Image Generation +1

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

1 code implementation6 Nov 2021 Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg

To this end, we propose a new multi-modality network (MultiModNet) for land cover mapping of multi-modal remote sensing data based on a novel pyramid attention fusion (PAF) module and a gated fusion unit (GFU).

Land Cover Classification

Discriminative Multimodal Learning via Conditional Priors in Generative Models

1 code implementation9 Oct 2021 Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen

Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data.

M3D-VTON: A Monocular-to-3D Virtual Try-On Network

1 code implementation ICCV 2021 Fuwei Zhao, Zhenyu Xie, Michael Kampffmeyer, Haoye Dong, Songfang Han, Tianxiang Zheng, Tao Zhang, Xiaodan Liang

Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value.

Virtual Try-on

Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification

1 code implementation20 May 2021 Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric Xing

In this work, we provide some theoretical insight into the properties of QNNs by presenting and analyzing a new form of invariance embedded in QNNs for both quantum binary classification and quantum representation learning, which we term negational symmetry.

Binary Classification Classification +1

Reconsidering Representation Alignment for Multi-view Clustering

1 code implementation CVPR 2021 Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer

Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering.

Clustering Contrastive Learning

Joint Optimization of an Autoencoder for Clustering and Embedding

1 code implementation7 Dec 2020 Ahcène Boubekki, Michael Kampffmeyer, Robert Jenssen, Ulf Brefeld

That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model able to jointly learn a clustering and an embedding.

Clustering Deep Clustering

Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data

no code implementations28 Nov 2020 Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Min Xu, Irina Voiculescu, Eric P. Xing

To bridge the methodological gaps in partially supervised learning (PSL) under data scarcity, we propose Vicinal Labels Under Uncertainty (VLUU), a simple yet efficient framework utilizing the human structure similarity for partially supervised medical image segmentation.

Data Augmentation Image Segmentation +5

Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series

1 code implementation16 Oct 2020 Kristoffer Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen

A measure of uncertainty in the relevance scores is computed by taking the standard deviation across the relevance scores produced by each model in the ensemble, which in turn is used to make the explanations more reliable.

Time Series Time Series Analysis

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

no code implementations3 Sep 2020 Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg

Capturing global contextual representations by exploiting long-range pixel-pixel dependencies has shown to improve semantic segmentation performance.

Graph Reconstruction Open-Ended Question Answering +2

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

1 code implementation15 Apr 2020 Luigi T. Luppino, Mads A. Hansen, Michael Kampffmeyer, Filippo M. Bianchi, Gabriele Moser, Robert Jenssen, Stian N. Anfinsen

We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces and reduce the impact of change pixels on the learning objective.

Change Detection Translation

Self-Constructing Graph Convolutional Networks for Semantic Labeling

1 code implementation15 Mar 2020 Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg

Here, we propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings and to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs.

Graph Reconstruction Knowledge Graphs

Dense Dilated Convolutions Merging Network for Land Cover Classification

1 code implementation9 Mar 2020 Qinghui Liu, Michael Kampffmeyer, Robert Jessen, Arnt-Børre Salberg

In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task.

Classification General Classification +2

Leveraging tensor kernels to reduce objective function mismatch in deep clustering

2 code implementations20 Jan 2020 Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer

In this work we study OFM in deep clustering, and find that the popular autoencoder-based approach to deep clustering can lead to both reduced clustering performance, and a significant amount of OFM between the reconstruction and clustering objectives.

Clustering Deep Clustering +1

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

no code implementations25 Sep 2019 Kristoffer Wickstrøm, Sigurd Løkse, Michael Kampffmeyer, Shujian Yu, Jose Principe, Robert Jenssen

In this paper, we propose an IP analysis using the new matrix--based R\'enyi's entropy coupled with tensor kernels over convolutional layers, leveraging the power of kernel methods to represent properties of the probability distribution independently of the dimensionality of the data.

Information Plane

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

no code implementations25 Sep 2019 Kristoffer Wickstrøm, Sigurd Løkse, Michael Kampffmeyer, Shujian Yu, Jose Principe, Robert Jenssen

In this paper, we propose an IP analysis using the new matrix--based R\'enyi's entropy coupled with tensor kernels over convolutional layers, leveraging the power of kernel methods to represent properties of the probability distribution independently of the dimensionality of the data.

Information Plane

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

no code implementations7 Sep 2019 Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg

This pushes the network towards learning more robust representations that are expected to boost the ultimate performance of the main task.

Computational Efficiency Multi-class Classification

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

1 code implementation30 Aug 2019 Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg

We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and complex shaped objects with similar color and textures, such as buildings, surfaces/roads, and trees in very high resolution remote sensing images.

Deep Generative Models for Reject Inference in Credit Scoring

1 code implementation12 Apr 2019 Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen

Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications.

Learning Latent Representations of Bank Customers With The Variational Autoencoder

no code implementations14 Mar 2019 Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen

We show that it is possible to steer the latent representations in the latent space of the VAE using the Weight of Evidence and forming a specific grouping of the data that reflects the customers' creditworthiness.

Clustering Management +1

Deep Divergence-Based Approach to Clustering

no code implementations13 Feb 2019 Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi, Lorenzo Livi, Arnt-Børre Salberg, Robert Jenssen

A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function.

Clustering Deep Clustering +1

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

no code implementations29 Nov 2018 Daniel J. Trosten, Andreas S. Strauman, Michael Kampffmeyer, Robert Jenssen

The task of clustering unlabeled time series and sequences entails a particular set of challenges, namely to adequately model temporal relations and variable sequence lengths.

Clustering Time Series +1

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

no code implementations29 Jul 2018 Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing

Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest.

whole slide images

The Deep Kernelized Autoencoder

no code implementations19 Jul 2018 Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi, Robert Jenssen, Lorenzo Livi

Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network.

Denoising

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

no code implementations10 Jul 2018 Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann, Eric Xing

Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models.

Zero-Shot Learning

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

no code implementations7 Jun 2018 Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen

We use the VAE and show that transforming the input data into a meaningful representation, it is possible to steer configurations in the latent space of the VAE.

Clustering Marketing

Dilated Temporal Relational Adversarial Network for Generic Video Summarization

no code implementations30 Apr 2018 Yu-jia Zhang, Michael Kampffmeyer, Xiaodan Liang, Dingwen Zhang, Min Tan, Eric P. Xing

Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner.

Generative Adversarial Network Video Summarization +1

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

no code implementations20 Apr 2018 Michael Kampffmeyer, Nanqing Dong, Xiaodan Liang, Yu-jia Zhang, Eric P. Xing

We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair based connectivity prediction task.

Relation Segmentation +1

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

no code implementations21 Sep 2017 Michael Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen

Techniques to improve urban land cover classification performance in remote sensing include fusion of data from different sensors with different data modalities.

Decision Making General Classification +2

Deep Kernelized Autoencoders

no code implementations8 Feb 2017 Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen, Lorenzo Livi

In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space.

Denoising

Temporal Overdrive Recurrent Neural Network

no code implementations18 Jan 2017 Filippo Maria Bianchi, Michael Kampffmeyer, Enrico Maiorino, Robert Jenssen

In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics.

Time Series Time Series Prediction

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