Results 31 to 40 of about 97,243 (265)
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
doaj +1 more source
Enhancing IoT Network Security: Unveiling the Power of Self-Supervised Learning against DDoS Attacks
Sensors, 2023 The Internet of Things (IoT), projected to exceed 30 billion active device connections globally by 2025, presents an expansive attack surface. The frequent collection and dissemination of confidential data on these devices exposes them to significant ...
Josue Genaro Almaraz-Rivera +2 more
doaj +1 more source
Remote Sensing, 2022 In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcrafted
Xiliang Chen, Guobin Zhu, Mingqing Liu
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Adversarial Masking for Self-Supervised Learning
CoRR, 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, Yuge +3 more
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Self-Supervised Node Classification with Strategy and Actively Selected Labeled Set
Entropy, 2022 To alleviate the impact of insufficient labels in less-labeled classification problems, self-supervised learning improves the performance of graph neural networks (GNNs) by focusing on the information of unlabeled nodes.
Yi Kang +3 more
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On the Stepwise Nature of Self-Supervised Learning
CoRR, 2023 9 pages (main text) + 14 pages (refs + appendices).
James B. Simon +5 more
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Mean Shift for Self-Supervised Learning [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Most recent self-supervised learning (SSL) algorithms learn features by contrasting between instances of images or by clustering the images and then contrasting between the image clusters. We introduce a simple mean-shift algorithm that learns representations by grouping images together without contrasting between them or adopting much of prior on the ...
Soroush Abbasi Koohpayegani +2 more
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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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