Results 11 to 20 of about 29,104 (263)
Few-Shot Few-Shot Learning and the role of Spatial Attention [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representation is learned on a limited amount of base class data, ignoring the amount of prior knowledge that a human may have accumulated before learning new tasks.
Lifchitz, Yann +2 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem.
Wenbin Li 0006 +6 more
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Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling is always time and resource consuming.
Chuxu Zhang +6 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Many real-world classification problems often have classes with very few labeled training samples. Moreover, all possible classes may not be initially available for training, and may be given incrementally. Deep learning models need to deal with this two-fold problem in order to perform well in real-life situations.
Pratik Mazumder +2 more
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Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023 Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients.
Song Wang 0013 +5 more
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Few-Shot Learning With Geometric Constraints [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 In this article, we consider the problem of few-shot learning for classification. We assume a network trained for base categories with a large number of training examples, and we aim to add novel categories to it that have only a few, e.g., one or five, training examples.
Honggyu Jung, Seong-Whan Lee
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Federated Few-Shot Learning with Adversarial Learning [PDF]
2021 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), 2021 We are interested in developing a unified machine learning model over many mobile devices for practical learning tasks, where each device only has very few training data. This is a commonly encountered situation in mobile computing scenarios, where data is scarce and distributed while the tasks are distinct.
Chenyou Fan, Jianwei Huang 0001
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Few-Shot Learning for Opinion Summarization [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents, such as user reviews of a product. The task is practically important and has attracted a lot of attention. However, due to the high cost of summary production, datasets large enough for training supervised models are lacking ...
Bražinskas, A., Lapata, M., Titov, I.
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Few-Shot Learning With Class Imbalance
IEEE Transactions on Artificial Intelligence, 2023 Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation. However, the standard training procedures overlook the real-world dynamics where classes commonly occur at different frequencies. While it is generally understood
Mateusz Ochal +4 more
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Semantic Matching Network for Few-Shot Learning [PDF]
Jisuanji gongcheng, 2021 In the field of deep learning,it is difficult to achieve visual recognition with a small number of samples.To address the problem,this paper proposes a semantic matching network.The dual attention mechanism is used to match the semantic information of ...
WANG Ronggui, TANG Mingkong, YANG Juan, XUE Lixia, HU Min
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