Results 21 to 30 of about 10,370,937 (299)
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
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Blended learning environments to foster self-directed learning [PDF]
, 2022 This book on blended learning environments to foster self-directed learning highlights the focus on research conducted in several teaching and learning contexts where blended learning had been implemented and focused on the fostering of self-directed ...
Ngwenya, Emmanuel +16 more
core +1 more source
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
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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
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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
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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
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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
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Biased Self-supervised Learning for ASR
INTERSPEECH 2023, 2023 Self-supervised learning via masked prediction pre-training (MPPT) has shown impressive performance on a range of speech-processing tasks. This paper proposes a method to bias self-supervised learning towards a specific task. The core idea is to slightly finetune the model that is used to obtain the target sequence. This leads to better performance and
Florian L. Kreyssig +5 more
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Self-Supervised Self-Supervision by Combining Deep Learning and Probabilistic Logic
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Labeling training examples at scale is a perennial challenge in machine learning. Self-supervision methods compensate for the lack of direct supervision by leveraging prior knowledge to automatically generate noisy labeled examples. Deep probabilistic logic (DPL) is a unifying framework for self-supervised learning that represents unknown labels as ...
Hunter Lang, Hoifung Poon
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Mixup Feature: A Pretext Task Self-Supervised Learning Method for Enhanced Visual Feature Learning
IEEE Access, 2023 Self-supervised learning has emerged as an increasingly popular research topic within the field of computer vision. In this study, we propose a novel self-supervised learning approach based on Mixup features as pretext tasks.
Jiashu Xu, Sergii Stirenko
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