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Found 4 papers, 3 papers with code
Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data
Lack of annotated data is a significant problem in machine learning, and Self-Supervised Learning (SSL) methods are typically used to deal with limited labelled data.
Survey on Self-supervised Representation Learning Using Image Transformations
Finally, we conclude with a summary of the results and insights as well as highlighting open problems to be addressed and indicating various future directions.
TransformNet: Self-supervised representation learning through predicting geometric transformations
The CNN trained on this pretext task will be used for the classification of images in the CIFAR-10 dataset as a downstream task.
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification
In our project, we extend the idea of using a VAE model for low dimensional latent space extraction with the self-supervised learning technique of feature subsetting.
Ranked #1 on
Cancer type classification
on TCGA
