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...
share
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...
research
∙
03/02/2023
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
Precise ischemic lesion segmentation plays an essential role in improvin...
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 ...
research
∙
10/15/2022
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
The need for interpretable models has fostered the development of self-e...
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 ...
research
∙
06/30/2022
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
Using decentralized data for federated training is one promising emergin...
research
∙
05/18/2022
The Kernelized Taylor Diagram
This paper presents the kernelized Taylor diagram, a graphical framework...
research
∙
03/30/2022
Automatic Identification of Chemical Moieties
In recent years, the prediction of quantum mechanical observables with m...
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...
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...
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...
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 ...
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)...
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...
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 ...
research
∙
05/20/2021
Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification
Entanglement is a physical phenomenon, which has fueled recent successes...
research
∙
03/13/2021
Reconsidering Representation Alignment for Multi-view Clustering
Aligning distributions of view representations is a core component of to...
research
∙
12/07/2020
Joint Optimization of an Autoencoder for Clustering and Embedding
Incorporating k-means-like clustering techniques into (deep) autoencoder...
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...
research
∙
09/03/2020
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation
Capturing global contextual representations by exploiting long-range pix...
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 ...
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...
research
∙
03/15/2020
Self-Constructing Graph Convolutional Networks for Semantic Labeling
Graph Neural Networks (GNNs) have received increasing attention in many ...
research
∙
03/09/2020
Dense Dilated Convolutions Merging Network for Land Cover Classification
Land cover classification of remote sensing images is a challenging task...
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...
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 ...
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 ...
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...
research
∙
03/14/2019
Learning Latent Representations of Bank Customers With The Variational Autoencoder
Learning data representations that reflect the customers' creditworthine...
research
∙
02/13/2019
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning rep...
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...
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...
research
∙
07/19/2018
The Deep Kernelized Autoencoder
Autoencoders learn data representations (codes) in such a way that the i...
research
∙
07/17/2018
Query-Conditioned Three-Player Adversarial Network for Video Summarization
Video summarization plays an important role in video understanding by se...
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 ...
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...
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...
research
∙
06/07/2018
Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder
Identifying customer segments in retail banking portfolios with differen...
research
∙
05/29/2018
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
The potential of graph convolutional neural networks for the task of zer...
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...
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...
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...
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 ...
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...
research
∙
02/08/2017
Deep Kernelized Autoencoders
In this paper we introduce the deep kernelized autoencoder, a neural net...
research
∙
01/18/2017
