Results 51 to 60 of about 546,017 (306)
Self-Supervised Learning for the Distinction between Computer-Graphics Images and Natural Images
Applied Sciences, 2023 With the increasing visual realism of computer-graphics (CG) images generated by advanced rendering engines, the distinction between CG images and natural images (NIs) has become an important research problem in the image forensics community.
Kai Wang
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Self-Writer: Clusterable Embedding Based Self-Supervised Writer Recognition from Unlabeled Data
Mathematics, 2022 Writer recognition based on a small amount of handwritten text is one of the most challenging deep learning problems because of the implicit characteristics of handwriting styles.
Zabir Mohammad +4 more
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Improvements to context based self-supervised learning
, 2018 We develop a set of methods to improve on the results of self-supervised learning using context. We start with a baseline of patch based arrangement context learning and go from there.
Chen, Barry Y. +2 more
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Self-Supervised Video Similarity Learning
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 We introduce S$^2$VS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to have strong transferability on target tasks after fine-tuning. Here, in contrast to prior work, SSL is used to perform video similarity learning and address multiple retrieval ...
Kordopatis-Zilos, Giorgos +5 more
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Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
, 2018 Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors: they usually produce results that ...
Chen, Huaijin +5 more
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Self-Supervised Representation Learning for Ultrasound Video
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020 Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to train the deep model. In practice, such annotations are expensive to collect and can be scarce for medical imaging applications.
Jaio, J +4 more
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Self-Supervised EEG Emotion Recognition Models Based on CNN
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023 Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning ...
Xingyi Wang +5 more
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Self-Supervised Adversarial Imitation Learning
2023 International Joint Conference on Neural Networks (IJCNN), 2023 This paper has been accepted in the International Joint Conference on Neural Networks (IJCNN ...
Monteiro, Juarez +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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Transitive Invariance for Self-supervised Visual Representation Learning
, 2017 Learning visual representations with self-supervised learning has become popular in computer vision. The idea is to design auxiliary tasks where labels are free to obtain.
Gupta, Abhinav +2 more
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