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Found 55 papers, 25 papers with code
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning.
M3D-VTON: A Monocular-to-3D Virtual Try-On Network
Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value.
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection.
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
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.
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 human-centric image generation due to its tremendous real-world potential.
Self-Constructing Graph Convolutional Networks for Semantic Labeling
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.
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 Graph Convolutional Networks (MSCG-Net) for semantic segmentation.
Reconsidering Representation Alignment for Multi-view Clustering
Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering.
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
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.
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images
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.
Dense Dilated Convolutions Merging Network for Land Cover Classification
In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task.
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
The lack of labeled data is a key challenge for learning useful representation from time series data.
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
The need for interpretable models has fostered the development of self-explainable classifiers.
Deep Generative Models for Reject Inference in Credit Scoring
Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications.
Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning
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.
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
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.
Leveraging tensor kernels to reduce objective function mismatch in deep clustering
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.
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
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.
Joint Optimization of an Autoencoder for Clustering and Embedding
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.
The Kernelized Taylor Diagram
Our proposed kernelized Taylor diagram is capable of visualizing similarities between populations with minimal assumptions of the data distributions.
Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks
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).
Discriminative Multimodal Learning via Conditional Priors in Generative Models
Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data.
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation
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.
Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder
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.
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
In this work, we propose a novel autoencoder architecture based on recurrent neural networks to generate compressed representations of MTS.
Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks
Techniques to improve urban land cover classification performance in remote sensing include fusion of data from different sensors with different data modalities.
Dilated Temporal Relational Adversarial Network for Generic Video Summarization
Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner.
ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation
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.
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 an important fraction of the electronic health records and are often both uncertain and incomplete.
Deep Kernelized Autoencoders
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.
Temporal Overdrive Recurrent Neural Network
In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics.
Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful
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.
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
Specifically, we propose a model that enforces our intuition that prediction masks should be domain independent.
Query-Conditioned Three-Player Adversarial Network for Video Summarization
Video summarization plays an important role in video understanding by selecting key frames/shots.
The Deep Kernelized Autoencoder
Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network.
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images
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.
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 particular set of challenges, namely to adequately model temporal relations and variable sequence lengths.
Deep Divergence-Based Approach to Clustering
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.
Learning Latent Representations of Bank Customers With The Variational Autoencoder
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.
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network
This pushes the network towards learning more robust representations that are expected to boost the ultimate performance of the main task.
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels
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.
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation
Capturing global contextual representations by exploiting long-range pixel-pixel dependencies has shown to improve semantic segmentation performance.
SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks
In this paper, we equip Prototypical Networks (PNs) with a novel dissimilarity measure to enable discriminative feature normalization for few-shot learning.
Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data
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.
Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification
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.
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
Current machine learning models have shown high efficiency in solving a wide variety of real-world problems.
Information Plane Analysis of Deep Neural Networks via Matrix--Based Renyi's Entropy and Tensor Kernels
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.
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 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.
Automatic Identification of Chemical Moieties
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes.
PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on
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.
Defending Against Poisoning Attacks in Federated Learning with Blockchain
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.
WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on
Image-based Virtual Try-On (VITON) aims to transfer an in-shop garment image onto a target person.
Data-Centric Machine Learning for Geospatial Remote Sensing Data
These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations
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.
