Semi-identical twins variational AutoEncoder for few-shot learning
Data augmentation is a popular way for few-shot learning (FSL). It generates more samples
as supplements and then transforms the FSL task into a common supervised learning …
as supplements and then transforms the FSL task into a common supervised learning …
Dizygotic conditional variational autoencoder for multi-modal and partial modality absent few-shot learning
Data augmentation is a powerful technique for improving the performance of the few-shot
classification task. It generates more samples as supplements, and then this task can be …
classification task. It generates more samples as supplements, and then this task can be …
Closed-form sample probing for learning generative models in zero-shot learning
Generative model based approaches have led to significant advances in zero-shot learning
(ZSL) over the past few years. These approaches typically aim to learn a conditional …
(ZSL) over the past few years. These approaches typically aim to learn a conditional …
Few-shot learning based on enhanced pseudo-labels and graded pseudo-labeled data selection
K Wang, X Wang, Y Cheng - … Journal of Machine Learning and Cybernetics, 2023 - Springer
Pseudo-labeled data is used to solve the data shortage in few-shot learning, in which the
quality of pseudo-labels and pseudo-labeled data selection determine the classification …
quality of pseudo-labels and pseudo-labeled data selection determine the classification …
Deep learning and neuroscience: a match made in heaven?
BY Choksi - 2022 - theses.hal.science
Deep Learning, as a field has tried to build networks that can perform intelligent tasks that
previously only humans could perform. While doing this, the field sets an ambitious goal of …
previously only humans could perform. While doing this, the field sets an ambitious goal of …