Results 71 to 80 of about 98,849 (283)
Applied Artificial IntelligenceUtilizing self-supervised learning to learn meaningful representations from unlabeled data can be a cost-effective strategy, particularly in medical domains where expert labeling incurs high costs.
Jihyo Kim +6 more
doaj +1 more source
Cross-modal Deep Metric Learning with Multi-task Regularization
, 2017 DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data ...
Huang, Xin, Peng, Yuxin
core +1 more source
Tumor mutational burden as a determinant of metastatic dissemination patterns
Molecular Oncology, EarlyView.This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal +4 more
wiley +1 more source
Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition: A Review
IEEE AccessDeep learning models have achieved significant success in human activity recognition, particularly in assisted living and telemonitoring. However, training these models requires substantial amounts of labeled training data, which is time-consuming and ...
Hui Chen +6 more
doaj +1 more source
Learning in Markov Random Fields with Contrastive Free Energies [PDF]
, 2005 Learning Markov random field (MRF) models is notoriously hard due to the presence of a global normalization factor. In this paper we present a new framework for learning MRF models based on the contrastive free energy (CF) objective function.
Sutton, Charles, Welling, Max
core +1 more source
Deep Metric Learning via Lifted Structured Feature Embedding
, 2015 Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolutional neural networks, recent works have shown promising results on ...
Jegelka, Stefanie +3 more
core +1 more source
Image Quality Assessment Using Contrastive Learning
IEEE Transactions on Image Processing, 2022 We consider the problem of obtaining image quality representations in a self-supervised manner. We use prediction of distortion type and degree as an auxiliary task to learn features from an unlabeled image dataset containing a mixture of synthetic and realistic distortions.
Pavan C. Madhusudana +4 more
openaire +3 more sources
Molecular Oncology, EarlyView.To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley +1 more source
VoxCeleb2: Deep Speaker Recognition
, 2018 The objective of this paper is speaker recognition under noisy and unconstrained conditions. We make two key contributions. First, we introduce a very large-scale audio-visual speaker recognition dataset collected from open-source media.
Chung, Joon Son +2 more
core +1 more source