Results 41 to 50 of about 943,879 (163)
Augmenting Few-Shot Learning With Supervised Contrastive Learning
IEEE Access, 2021 Few-shot learning deals with a small amount of data which incurs insufficient performance with conventional cross-entropy loss. We propose a pretraining approach for few-shot learning scenarios.
Taemin Lee, Sungjoo Yoo
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International Journal of Applied Mathematics and Computer Science, 2023 Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patients.
Casalino Gabriella +6 more
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Biological PsychologyThis paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move - in the ensuing schemes - is to place priors on the selection of models, based upon expected free energy.
Karl J. Friston +12 more
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Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning
IEEE Access, 2021 While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training.
Mayank Golhar +5 more
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Unsupervised end-to-end training with a self-defined target
Neuromorphic Computing and EngineeringDesigning algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phases of self-supervised and supervised learning are accurate and adaptable to
Dongshu Liu +4 more
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Semi-supervised transductive speaker identification [PDF]
, 2009 We present an application of transductive semi-supervised learning to the problem of speaker identification. Formulating this problem as one of transduction is the most natural choice in some scenarios, such as when annotating archived speech data ...
Täckström, Oscar
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Gated Self-supervised Learning for Improving Supervised Learning
2024 IEEE Conference on Artificial Intelligence (CAI)In past research on self-supervised learning for image classification, the use of rotation as an augmentation has been common. However, relying solely on rotation as a self-supervised transformation can limit the ability of the model to learn rich features from the data.
Fuadi, Erland Hilman +3 more
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To Compress or Not to Compress—Self-Supervised Learning and Information Theory: A Review
EntropyDeep neural networks excel in supervised learning tasks but are constrained by the need for extensive labeled data. Self-supervised learning emerges as a promising alternative, allowing models to learn without explicit labels.
Ravid Shwartz Ziv, Yann LeCun
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CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW
Iraqi Journal for Computers and Informatics, 2021 Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data.
Aska Ezadeen Mehyadin +1 more
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Domain Generalization by Solving Jigsaw Puzzles
, 2019 Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own.
Bucci, Silvia +4 more
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