Results 71 to 80 of about 29,104 (263)

Few-Shot Learning With a Strong Teacher

IEEE Transactions on Pattern Analysis and Machine Intelligence
Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing works take the meta-learning approach, constructing a few-shot learner that can learn from few-shot examples to generate a classifier. Typically, the few-shot learner is constructed or meta-trained by sampling multiple few-shot tasks in turn and ...
Han-Jia Ye, Lu Ming, De-Chuan Zhan, Wei-Lun Chao   +3 more
openaire   +3 more sources

Relational Generalized Few-Shot Learning

CoRR, 2019
Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has received a lot of attention recently, most proposed methods focus on discriminating novel classes only.
Xiahan Shi, Leonard Salewski, Martin Schiegg, Zeynep Akata, Max Welling   +4 more
openaire   +2 more sources

Few Shot Learning for Information Verification

CoRR, 2021
Information verification is quite a challenging task, this is because many times verifying a claim can require picking pieces of information from multiple pieces of evidence which can have a hierarchy of complex semantic relations. Previously a lot of researchers have mainly focused on simply concatenating multiple evidence sentences to accept or ...
Usama Khalid, Mirza Omer Beg
openaire   +2 more sources

Few shot learning for Korean winter temperature forecasts

npj Climate and Atmospheric Science
To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation
Seol-Hee Oh, Yoo-Geun Ham
doaj   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

Recycling of NiTi Shape Memory Alloys: Fundamental and Technological Aspects of a Vacuum Induction Melting Processing Route

Advanced Engineering Materials, EarlyView.
The present study investigates recycling of NiTi shape memory alloys via vacuum induction melting. An ingot was synthesized from elemental Ni and Ti and subjected to three subsequent remelting cycles. Remelting increases process durations and impurity levels and adversely affects microstructures and functional properties.
Sakia Sophia Noorzayee, Son Tung Mai, Sarath Menon, Braulio Gadiel Sánchez Ortega, Ralf Drautz, Alexander Kauffmann, Gunther Eggeler, Jan Frenzel   +7 more
wiley   +1 more source

Proxy Network for Few Shot Learning

CoRR, 2020
The use of a few examples for each class to train a predictive model that can be generalized to novel classes is a crucial and valuable research direction in artificial intelligence. This work addresses this problem by proposing a few-shot learning (FSL) algorithm called proxy network under the architecture of meta-learning.
Bin Xiao 0008, Chien-Liang Liu, Wen-Hoar Hsaio   +2 more
openaire   +3 more sources

Constellation loss: Improving the efficiency of deep metric learning loss functions for the optimal embedding of histopathological images

Journal of Pathology Informatics, 2020
Background: Deep learning diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, and they still require a huge amount of well-annotated data for training, which is often non affordable.
Alfonso Medela, Artzai Picon
doaj   +1 more source

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus Stricker   +5 more
wiley   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source