Results 11 to 20 of about 8,885,948 (291)

Few-shot Classifier GAN [PDF]

, 2018
Fine-grained image classification with a few-shot classifier is a highly challenging open problem at the core of a numerous data labeling applications.
Adamu Ali-Gombe (19726213), Chrisina Jayne (17919416), Yann Savoye (19645732), Eyad Elyan (17296126)   +3 more
core   +6 more sources

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, Avrithis, Yannis, Picard, Sylvaine   +2 more
openaire   +3 more sources

Few-Shot Learning on Graphs

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, Kaize Ding, Jundong Li, Xiangliang Zhang 0001, Yanfang Ye 0001, Nitesh V. Chawla, Huan Liu 0001   +6 more
openaire   +3 more sources

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.
Pratik Mazumder, Pravendra Singh, Piyush Rai   +2 more
openaire   +4 more sources

MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification [PDF]

, 2023
In the medical research domain, limited data and high annotation costs have made efficient classification under few-shot conditions a popular research area.
Lu, Liangfu, Xudong Cui, Yulei Wu, Wu, Yulei, Liangfu Lu, Zhiyuan Tan, Tan, Zhiyuan, Cui, Xudong   +7 more
core   +1 more source

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 0013, Xingbo Fu, Kaize Ding, Chen Chen 0022, Huiyuan Chen, Jundong Li   +5 more
openaire   +3 more sources

Defensive Few-shot Learning

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, Lei Wang 0001, Xingxing Zhang 0001, Lei Qi 0001, Jing Huo, Yang Gao 0001, Jiebo Luo 0001   +6 more
openaire   +5 more sources

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
openaire   +4 more sources

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
openaire   +2 more sources

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, Vineeth N. Balasubramanian, C. V. Jawahar   +2 more
openaire   +2 more sources