Results 21 to 30 of about 8,885,948 (291)
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 for Domain-specific Fine-grained Image Classification
, 2020 Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world industrial applications.
Qiong Li (346270) +5 more
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Few‐shot learning with relation propagation and constraint
IET Computer Vision, 2021 Previous deep learning methods usually required large‐scale annotated data, which is computationally exhaustive and unrealistic in certain scenarios. Therefore, few‐shot learning, where only a few annotated training images are available for training, has
Huiyun Gong +6 more
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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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Learning multi-level weight-centric features for few-shot learning
, 2022 Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. Contemporary approaches based on weight-generation scheme delivers a straightforward and flexible solution to the problem ...
Liang, Mingjiang +4 more
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RankDNN: Learning to Rank for Few-Shot Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic.
Qianyu Guo +6 more
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Few-Shot Remote Sensing Image Classification with Meta-Learning
, 2023 The performance of machine learning models relies on the quality, quantity, and diversity of annotated remote sensing datasets. However, the expensive effort required to annotate samples from diverse locations around the globe, coupled with the need for ...
Morris Riedel (11132848) +3 more
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Incrementally Learned Angular Representations for Few-Shot Class-Incremental Learning
IEEE Access, 2023 The main challenge of FSCIL is the trade-off between underfitting to a new session task and preventing forgetting the knowledge for earlier sessions. In this paper, we reveal that the angular space occupied by the features within the embedded area is ...
In-Ug Yoon, Jong-Hwan Kim
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Augmenting Few-Shot Learning With Supervised Contrastive Learning
IEEE Access, 2021 Few-shot learning deals with a small amount of data which incurs insufficient performance with conventional cross-entropy loss. We propose a pretraining approach for few-shot learning scenarios.
Taemin Lee, Sungjoo Yoo
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Interventional Few-Shot Learning
CoRR, 2020 We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This finding is rooted from our causal assumption: a Structural Causal Model (SCM) for the causalities among the pre-trained knowledge, sample features, and labels.
YUE, Zhongqi +3 more
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