Results 21 to 30 of about 29,104 (263)
Few Shot Learning With No Labels
CoRR, 2020 Few-shot learners aim to recognize new categories given only a small number of training samples. The core challenge is to avoid overfitting to the limited data while ensuring good generalization to novel classes. Existing literature makes use of vast amounts of annotated data by simply shifting the label requirement from novel classes to base classes ...
Aditya Bharti +2 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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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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Unsupervised Few-Shot Feature Learning via Self-Supervised Training
Frontiers in Computational Neuroscience, 2020 Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community.
Zilong Ji +4 more
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Feature Transformation Network for Few-Shot Learning
IEEE Access, 2021 Few-shot learning researches to learn a novel concept from a handful of labeled samples. Due to the small amount of training data, deep network has the risk of over-fitting. Although many previous approaches based on metric criterion can make significant
Xiaoyan Wang, Hongmei Wang, Daming Zhou
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Applying a Probabilistic Network Method to Solve Business-Related Few-Shot Classification Problems
Complexity, 2021 It can be challenging to learn algorithms due to the research of business-related few-shot classification problems. Therefore, in this paper, we evaluate the classification of few-shot learning in the commercial field.
Lang Wu, Menggang Li
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Improving Augmentation Efficiency for Few-Shot Learning
IEEE Access, 2022 While human intelligence can easily recognize some characteristics of classes with one or few examples, learning from few examples is a challenging task in machine learning.
Wonhee Cho, Eunwoo Kim
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Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning
Frontiers in Neuroscience, 2022 Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the major challenges of current machine learning techniques, including the high energy consumption induced by deep neural networks.
Shuangming Yang +2 more
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