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Meta learning for few shot learning [PDF]
, 2022 Few-shot learning aims to scale visual recognition to open-ended growth of new classes with limited labelled examples, thus alleviating data and computation bottleneck of conventional deep learning. This thesis proposes a meta learning (a.k.a.
Zhang, Xueting
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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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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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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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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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Few Shot Learning with Simplex
CoRR, 2018 Deep learning has made remarkable achievement in many fields. However, learning the parameters of neural networks usually demands a large amount of labeled data. The algorithms of deep learning, therefore, encounter difficulties when applied to supervised learning where only little data are available. This specific task is called few-shot learning.
Zhang, Bowen +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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