Results 21 to 30 of about 97,243 (265)
Mixture of Self-Supervised Learning
CoRR, 2023 Self-supervised learning is popular method because of its ability to learn features in images without using its labels and is able to overcome limited labeled datasets used in supervised learning. Self-supervised learning works by using a pretext task which will be trained on the model before being applied to a specific task. There are some examples of
Aristo Renaldo Ruslim +2 more
openaire +2 more sources
A self-supervised deep learning method for data-efficient training in genomics
Communications Biology, 2023 Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine ...
Hüseyin Anil Gündüz +7 more
doaj +1 more source
Self-Supervised Representation Learning for Document Image Classification
IEEE Access, 2021 Supervised learning, despite being extremely effective, relies on expensive, time-consuming, and error-prone annotations. Self-supervised learning has recently emerged as a strong alternate to supervised learning in a range of different domains as ...
Shoaib Ahmed Siddiqui +2 more
doaj +1 more source
Credal Self-Supervised Learning
CoRR, 2021 Self-training is an effective approach to semi-supervised learning. The key idea is to let the learner itself iteratively generate "pseudo-supervision" for unlabeled instances based on its current hypothesis. In combination with consistency regularization, pseudo-labeling has shown promising performance in various domains, for example in computer ...
Julian Lienen, Eyke Hüllermeier
openaire +3 more sources
Self-Supervised Dialogue Learning [PDF]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 11pages, 2 figures, accepted to ACL ...
Jiawei Wu 0003 +2 more
openaire +2 more sources
Self-Supervised Learning for Segmentation
CoRR, 2021 Self-supervised learning is emerging as an effective substitute for transfer learning from large datasets. In this work, we use kidney segmentation to explore this idea. The anatomical asymmetry of kidneys is leveraged to define an effective proxy task for kidney segmentation via self-supervised learning. A siamese convolutional neural network (CNN) is
Abhinav Dhere, Jayanthi Sivaswamy
openaire +2 more sources
DenseCL: A simple framework for self-supervised dense visual pre-training
Visual Informatics, 2023 Self-supervised learning aims to learn a universal feature representation without labels. To date, most existing self-supervised learning methods are designed and optimized for image classification.
Xinlong Wang +3 more
doaj +1 more source
Audio self-supervised learning: A survey
Patterns, 2022 Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations, which is an expensive and time consuming task.
Shuo Liu 0012 +7 more
openaire +3 more sources
A Survey on Contrastive Self-Supervised Learning [PDF]
Technologies, 2020 Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks.
Ashish Jaiswal +4 more
openaire +3 more sources
Brain Sciences, 2021 Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning ...
Jingwei Li +6 more
doaj +1 more source