Results 31 to 40 of about 517,396 (274)
Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning
Frontiers in Neuroscience, 2022 Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the major challenges of current machine learning techniques, including the high energy consumption induced by deep neural networks.
Shuangming Yang +2 more
doaj +1 more source
Graph Few-shot Learning via Knowledge Transfer
, 2020 Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating ...
Chawla, Nitesh V. +7 more
core +1 more source
Active Few-Shot Learning with FASL
, 2022 Recent advances in natural language processing (NLP) have led to strong text classification models for many tasks. However, still often thousands of examples are needed to train models with good quality. This makes it challenging to quickly develop and deploy new models for real world problems and business needs.
Thomas Müller +3 more
openaire +2 more sources
Improving Augmentation Efficiency for Few-Shot Learning
IEEE Access, 2022 While human intelligence can easily recognize some characteristics of classes with one or few examples, learning from few examples is a challenging task in machine learning.
Wonhee Cho, Eunwoo Kim
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Few-Shot Classification with Contrastive Learning
, 2022 To appear in ECCV ...
Zhanyuan Yang +2 more
openaire +2 more sources
Few-Shot and Zero-Shot Learning for Historical Text Normalization [PDF]
, 2019 Historical text normalization often relies on small training datasets. Recent work has shown that multi-task learning can lead to significant improvements by exploiting synergies with related datasets, but there has been no systematic study of different ...
Bollmann, Marcel +2 more
core +3 more sources
RankDNN: Learning to Rank for Few-Shot Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic.
Guo, Qianyu +6 more
openaire +2 more sources
Robust Compare Network for Few-Shot Learning
IEEE Access, 2020 Making machines learn like humans is the ultimate goal of artificial intelligence. Few-shot learning attempts to simulate the learning mechanism of humans, which is a task that can learn novel concepts from very few labeled samples.
Yixin Yang +4 more
doaj +1 more source
Learning few-shot imitation as cultural transmission
Nature Communications, 2023 Cultural transmission is the domain-general social skill that allows agents to acquire and use information from each other in real-time with high fidelity and recall. It can be thought of as the process that perpetuates fit variants in cultural evolution.
Avishkar Bhoopchand +17 more
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VEHICLE DETECTION AND IDENTIFICATION WITH SMALL DATASET USING FEW-SHOT LEARNING
International Journal of Advances in Signal and Image Sciences, 2023 Vehicle detection and identification serve an important role in employing autonomous vehicle classification. However, most deep learning methods for vehicle detection rely on large number of datasets for the training to perform well. The dataset shortage
Muzakki Afandi, Media Anugerah Ayu
doaj +1 more source