Results 31 to 40 of about 31,762 (259)
Weakly Supervised Dictionary Learning
IEEE Transactions on Signal Processing, 2018 We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis ...
Zeyu You +3 more
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Sensors, 2021 Recent advances in deep learning models for image interpretation finally made it possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large datasets of
Suzanna Cuypers +2 more
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MetaFL: Metamorphic fault localisation using weakly supervised deep learning
IET Software, 2023 Deep‐Learning‐based Fault Localisation (DLFL) leverages deep neural networks to learn the relationship between statement behaviour and program failures, showing promising results.
Lingfeng Fu +5 more
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Weakly Supervised Object Boundaries [PDF]
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of training data.
Anna Khoreva +4 more
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Weakly supervised learning of allomorphy [PDF]
Proceedings of the First Workshop on Subword and Character Level Models in NLP, 2017 Most NLP resources that offer annotations at the word segment level provide morphological annotation that includes features indicating tense, aspect, modality, gender, case, and other inflectional information. Such information is rarely aligned to the relevant parts of the words—i.e. the allomorphs, as such annotation would be very costly.
Miikka Silfverberg, Mans Hulden
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Weakly Supervised Disentanglement with Guarantees
CoRR, 2019 Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporation of weak supervision.
Rui Shu +4 more
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Weakly-supervised Appraisal Analysis [PDF]
Linguistic Issues in Language Technology, 2012 This article is concerned with the computational treatment of Appraisal, a Systemic Functional Linguistic theory of the types of language employed to communicate opinion in English. The theory considers aspects such as Attitude (how writers communicate their point of view), Engagement (how writers align themselves with respect to the opinions of others)
Read, Jonathon Lee, Carroll, John
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Weakly Supervised Learning of Affordances
CoRR, 2016 Localizing functional regions of objects or affordances is an important aspect of scene understanding. In this work, we cast the problem of affordance segmentation as that of semantic image segmentation. In order to explore various levels of supervision, we introduce a pixel-annotated affordance dataset of 3090 images containing 9916 object instances ...
Abhilash Srikantha, Juergen Gall
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Weakly-Supervised Learning of Human Dynamics [PDF]
, 2020 This paper proposes a weakly-supervised learning framework for dynamics estimation from human motion. Although there are many solutions to capture pure human motion readily available, their data is not sufficient to analyze quality and efficiency of movements.
Petrissa Zell +2 more
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Weakly Supervised Causal Representation Learning
Advances in Neural Information Processing Systems 35, 2022 Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under mild assumptions that this representation is however identifiable in a weakly supervised setting. This involves a dataset with paired samples before and after random, unknown
Johann Brehmer +3 more
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