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Few-Shot Learning With a Strong Teacher

IEEE Transactions on Pattern Analysis and Machine Intelligence
Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing works take the meta-learning approach, constructing a few-shot learner that can learn from few-shot examples to generate a classifier. Typically, the few-shot learner is constructed or meta-trained by sampling multiple few-shot tasks in turn and ...
Han-Jia Ye, Lu Ming, De-Chuan Zhan, Wei-Lun Chao   +3 more
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HCPNet: Learning discriminative prototypes for few-shot remote sensing image scene classification

International Journal of Applied Earth Observations and Geoinformation, 2023
Few-shot learning is an important and challenging research topic for remote sensing image scene classification. Many existing approaches address this challenge by using meta-learning and metric-learning techniques, which aim to develop feature extractors
Junjie Zhu, Ke Yang, Naiyang Guan, Xiaodong Yi, Chunping Qiu   +4 more
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A Few-shot Learning Model based on a Triplet Network for the Prediction of Energy Coincident Peak Days

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
In an electricity system, a coincident peak (CP) is defined as the highest daily power demand in a year, which plays an important role in keeping the balance between power supply and its demand.
Jinxiang Liu, Laura Brown
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Few-Shot Partial Multi-View Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity.
Yuan Zhou, Yanrong Guo, Shijie Hao, Richang Hong, Jiebo Luo   +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
doaj   +1 more source

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, Massimiliano Patacchiola, Jose Vazquez, Amos Storkey, Sen Wang   +4 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, Shuo Wang, Xiaowei Zhao, Yifan Yan, Yuqing Ma, Wei Liu, Xianglong Liu   +6 more
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Few-Shot Lifelong Learning

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.
Mazumder, Pratik, Singh, Pravendra, Rai, Piyush   +2 more
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Federated Few-shot Learning

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, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li   +5 more
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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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