Results 41 to 50 of about 8,885,948 (291)

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, Bernabe Linares-Barranco, Badong Chen   +2 more
doaj   +1 more source

AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms

, 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, Chilese, Marco, Goekbakan, Bora, Dmitrienko, Alexandra, Sadeghi, Ahmad-Reza, Fereidooni, Hossein, König, Jan, Rieger, Phillip   +7 more
core   +1 more source

Few-Shot Classification with Contrastive Learning

, 2022
To appear in ECCV ...
Zhanyuan Yang, Jinghua Wang, Yingying Zhu 0001   +2 more
openaire   +4 more sources

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, Bethanie Brownfield, Adrian Collister, Agustin Dal Lago, Ashley Edwards, Richard Everett, Alexandre Fréchette, Yanko Gitahy Oliveira, Edward Hughes, Kory W. Mathewson, Piermaria Mendolicchio, Julia Pawar, Miruna Pȋslar, Alex Platonov, Evan Senter, Sukhdeep Singh, Alexander Zacherl, Lei M. Zhang   +17 more
doaj   +1 more source

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
core   +1 more source

Defensive Few-shot Learning [PDF]

, 2023
This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks.
Luo, Jiebo, Zhang, Xingxing, Qi, Lei, Gao, Yang, Wang, Lei, Huo, Jing, Li, Wenbin   +6 more
core   +1 more source

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
core   +1 more source

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, Yang Li, Rui Zhang, Jiabao Wang, Zhuang Miao   +4 more
doaj   +1 more source

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
doaj   +1 more source

Pixels to pitch: extending few-shot learning to audio tasks [PDF]

Few-Shot Learning has gained significant attention in recent years as a possible tool for solving tasks which have too little data for traditional machine learning pipelines, such as user adaptable AI systems or rare event detection. At the start of this
Heggan, Calum
core   +1 more source