In the era of deep learning, federated learning (FL) presents a promisin...
In prediction of forest parameters with data from remote sensing (RS),
r...
Precise ischemic lesion segmentation plays an essential role in improvin...
In this paper, we target image-based person-to-person virtual try-on in ...
The need for interpretable models has fostered the development of
self-e...
Image-based virtual try-on is one of the most promising applications of
...
Using decentralized data for federated training is one promising emergin...
This paper presents the kernelized Taylor diagram, a graphical framework...
In recent years, the prediction of quantum mechanical observables with
m...
The lack of labeled data is a key challenge for learning useful
represen...
Recent work has shown that label-efficient few-shot learning through
sel...
The recent trend of integrating multi-source Chest X-Ray datasets to imp...
Image-based virtual try-on is one of the most promising applications of
...
Multi-modality data is becoming readily available in remote sensing (RS)...
Deep generative models with latent variables have been used lately to le...
Current machine learning models have shown high efficiency in solving a ...
Entanglement is a physical phenomenon, which has fueled recent successes...
Aligning distributions of view representations is a core component of to...
Incorporating k-means-like clustering techniques into (deep) autoencoder...
The data-driven nature of deep learning models for semantic segmentation...
Capturing global contextual representations by exploiting long-range
pix...
We propose a novel architecture called the Multi-view Self-Constructing ...
Image translation with convolutional autoencoders has recently been used...
Graph Neural Networks (GNNs) have received increasing attention in many
...
Land cover classification of remote sensing images is a challenging task...
Image translation with convolutional neural networks has recently been u...
Analyzing deep neural networks (DNNs) via information plane (IP) theory ...
It is important, but challenging, for the forest industry to accurately ...
We propose a network for semantic mapping called the Dense Dilated
Convo...
Learning data representations that reflect the customers' creditworthine...
A promising direction in deep learning research consists in learning
rep...
The task of clustering unlabeled time series and sequences entails a
par...
Convolutional neural networks have led to significant breakthroughs in t...
Autoencoders learn data representations (codes) in such a way that the i...
Video summarization plays an important role in video understanding by
se...
Convolutional Neural Networks (CNNs) are propelling advances in a range ...
Raven's Progressive Matrices are one of the widely used tests in evaluat...
The cardiothoracic ratio (CTR), a clinical metric of heart size in chest...
Identifying customer segments in retail banking portfolios with differen...
The potential of graph convolutional neural networks for the task of
zer...
Learning compressed representations of multivariate time series (MTS)
fa...
The large amount of videos popping up every day, make it is more and mor...
Salient segmentation aims to segment out attention-grabbing regions, a
c...
Clinical measurements that can be represented as time series constitute ...
Automatic urban land cover classification is a classical problem in remo...
In this paper we introduce the deep kernelized autoencoder, a neural net...
In this work we present a novel recurrent neural network architecture
de...