Results 31 to 40 of about 546,017 (306)
Comparing Learning Methodologies for Self-Supervised Audio-Visual Representation Learning
IEEE Access, 2022 In recent years, the machine learning community has devoted an increasing attention to self-supervised learning.The performance gap between supervised and self-supervised has become increasingly narrow in many computer vision applications. In this paper,
Hacene Terbouche +3 more
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Self-supervised Learning for Spinal MRIs [PDF]
, 2017 A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs).
Jamaludin, A, Kadir, T, Zisserman, A
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Self-supervised Learning: A Succinct Review
Archives of Computational Methods in Engineering, 2023 Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data.
Veenu Rani +4 more
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Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction [PDF]
, 2019 In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.
Bai, W. +8 more
core +4 more sources
PackerRobo: Model-based robot vision self supervised learning in CART
Alexandria Engineering Journal, 2022 Robots are most widely used to replace human contribution with machine generated response. When humans interact with robots, its mandatory for both to forecast actions based on current conditions. Huge efforts have been channelized towards attaining this
Asif Khan +8 more
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Review of Self-supervised Learning Methods in Field of ECG [PDF]
Jisuanji kexue yu tansuoDeep learning has been widely applied in the field of electrocardiogram (ECG) signal analysis due to its powerful data representation capability. However, supervised methods require a large amount of labeled data, and ECG data annotation is typically ...
HAN Han, HUANG Xunhua, CHANG Huihui, FAN Haoyi, CHEN Peng, CHEN Jijia
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Reduce the Difficulty of Incremental Learning With Self-Supervised Learning
IEEE Access, 2021 Incremental learning requires a learning model to learn new tasks without forgetting the learned tasks continuously. However, when a deep learning model learns new tasks, it will catastrophically forget tasks it has learned before.
Linting Guan, Yan Wu
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Synergistic Self-supervised and Quantization Learning
, 2022 With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream tasks. However, we find that current SSL models suffer severe accuracy drops when performing low-bit quantization, prohibiting their deployment in resource-constrained ...
Yun-Hao Cao +4 more
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Self-Distilled Self-supervised Representation Learning
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 WACV 23, 11 ...
Jang, Jiho +5 more
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Adversarial Masking for Self-Supervised Learning
, 2022 We propose ADIOS, a masked image model (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial objective. The image encoder is trained to minimise the distance between representations of the original and that of a masked image.
Shi, Y +3 more
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