Results 51 to 60 of about 10,284,296 (292)
Few-Shot Partial-Label Learning [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient partial-label (PL) samples for training.
Yunfeng Zhao +5 more
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Few-shot Classification via Ensemble Learning with Multi-Order Statistics
, 2023 Transfer learning has been widely adopted for few-shot classification. Recent studies reveal that obtaining good generalization representation of images on novel classes is the key to improving the few-shot classification accuracy.
Yang, S, Liu, F, Zhou, J, Chen, D
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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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Few-shot Learning for Domain-specific Fine-grained Image Classification
, 2020 Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world industrial applications.
Qiong Li (346270) +5 more
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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
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Demystification of Few-shot and One-shot Learning
, 2021 Few-shot and one-shot learning have been the subject of active and intensive research in recent years, with mounting evidence pointing to successful implementation and exploitation of few-shot learning algorithms in practice.
Tyukin, Ivan Y. +3 more
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Adaptive Learning Knowledge Networks for Few-Shot Learning
IEEE Access, 2019 In recent years, relying on training with thousands of labeled samples, deep learning has achieved remarkable success in the field of computer vision. However, in practice, annotating samples is a time-consuming and laborious task, which means that it is
Minghao Yan
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Few-Shot Class-Incremental Learning [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the ...
Xiaoyu Tao +5 more
openaire +4 more sources
, 2023 Mobile applications are widely used for online services sharing a large amount of personal data online. One-time authentication techniques such as passwords and physiological biometrics (e.g., fingerprint, face, and iris) have their own advantages but ...
Finke, Moritz +15 more
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Catalysis distillation neural network for the few-shot open catalyst challenge [PDF]
, 2023 The integration of artificial intelligence and science has resulted in substantial progress in computational chemistry methods for the design and discovery of novel catalysts.
Bowen, Deng
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