Results 11 to 20 of about 16,806 (262)
Advances in Neural Information Processing Systems 36, 2023 Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it.
Hamed Nilforoshan +7 more
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RS-CLIP: Zero shot remote sensing scene classification via contrastive vision-language supervision
International Journal of Applied Earth Observations and Geoinformation, 2023 Zero-shot remote sensing scene classification aims to solve the scene classification problem on unseen categories and has attracted numerous research attention in the remote sensing field.
Xiang Li +3 more
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Zero-shot Image Classification Method Based on Discriminator Feedback
Journal of Harbin University of Science and Technology, 2023 Zero-shot learning (ZSL) strives to classify unseen categories for which no data is available during training.At present, among generative methods, zero-shot learning based on joint generative model VAEGAN is a research hotspot.On this basis, we propose ...
FAN Yufei, DING Bo, HE Yongjun
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Variational Disentangle Zero-Shot Learning
Mathematics, 2023 Existing zero-shot learning (ZSL) methods typically focus on mapping from the feature space (e.g., visual space) to class-level attributes, often leading to a non-injective projection.
Jie Su +4 more
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Dual Generative Network with Discriminative Information for Generalized Zero-Shot Learning
Complexity, 2021 Zero-shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero-shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes ...
Tingting Xu, Ye Zhao, Xueliang Liu
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Zero-Shot Image Classification Based on Improved Variational Auto-encoder
Taiyuan Ligong Daxue xuebao, 2021 In the process of zero-shot image classification, problems such as high acquisition cost for samples of known categories and domain drift were addressed.
Zhen CAO, Hongwei XIE
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Bayesian Zero-Shot Learning [PDF]
, 2020 Object classes that surround us have a natural tendency to emerge at varying levels of abstraction. We propose a Bayesian approach to zero-shot learning (ZSL) that introduces the notion of meta-classes and implements a Bayesian hierarchy around these classes to effectively blend data likelihood with local and global priors.
Badirli, Sarkhan +2 more
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Zero-shot program representation learning
Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, 2022 In 30th International Conference on Program Comprehension (ICPC 22), May 16 to 17, 2022, Virtual Event ...
Nan Cui +3 more
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Ordinal Zero-Shot Learning [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Zero-shot learning predicts new class even if no training data is available for that class. The solution to conventional zero-shot learning usually depends on side information such as attribute or text corpora. But these side information is not easy to obtain or use.
Zeng-Wei Huo, Xin Geng 0001
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Zero-Shot Kernel Learning [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 IEEE Conference on Computer Vision and Pattern Recognition ...
Hongguang Zhang, Piotr Koniusz
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