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Found 5 papers, 3 papers with code
Can Generative Models Improve Self-Supervised Representation Learning?
By directly conditioning generative models on a source image representation, our method enables the generation of diverse augmentations while maintaining the semantics of the source image, thus offering a richer set of data for self-supervised learning.
FENDA-FL: Personalized Federated Learning on Heterogeneous Clinical Datasets
Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning models in clinical settings.
Class Impression for Data-free Incremental Learning
Here, we propose a novel data-free class incremental learning framework that first synthesizes data from the model trained on previous classes to generate a \ours.
SVG-Net: An SVG-based Trajectory Prediction Model
Finally, we illustrate how, by using SVG, one can benefit from datasets and advancements in other research fronts that also utilize the same input format.
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI
Fazekas scale facilitates an accurate quantitative assessment of the severity of white matter lesions and hence the disease.
