Affiliation:
1. School of Mathematics, Physics and Statistics Shanghai University of Engineering Science Shanghai China
Abstract
AbstractFew‐shot learning enables machines to learn efficiently from limited labelled data. However, existing few‐shot learning methods may perform poorly when there is a lack of sufficient samples, and may encounter problems such as domain shift or overfitting when applied to new domains or tasks. To address the issues of poor fitting and insufficient generalization ability in new domains, a new method called triplet merged network with involution operators (TMNIO) is proposed. This method employs dual encoders that extract common and distinctive features from the prototype network, thereby enhancing the model's feature extraction capability. To further improve this ability, the traditional convolutional kernels are replaced with involution operators, which not only reduce the parameter count but also enlarge the receptive field to better extract local feature information. Additionally, this method employs a two‐stage training strategy, where triplet loss is used in the first stage to train the model and enhance its robustness and generalization ability. Extensive experiments on the miniImageNet, Omniglot, and Caltech‐UCSD Birds‐200 (CUB) datasets have shown that our proposed method achieved significant improvements in both training speed and accuracy, particularly on the miniImageNet dataset, where it achieved an outstanding 10% performance improvement.
Publisher
Institution of Engineering and Technology (IET)
Reference36 articles.
1. Vinyals O. Blundell C. Lillicrap T. Wierstra D.:Matching networks for one shot learning.Advances in Neural Information Processing Systems 29 (NIPS 2016) vol.29.Curran Associates Red Hook NY(2016)
2. Andrychowicz M. Denil M. Gomez S. Hoffman M.W. Pfau D. Schaul T. et al.:Learning to learn by gradient descent by gradient descent.Advances in Neural Information Processing Systems 29 (NIPS 2016) vol.29.Curran Associates Red Hook NY(2016)
3. Finn C. Abbeel P. Levine S.:Model‐agnostic meta‐learning for fast adaptation of deep networks. In:Proceedings of the 34th International Conference on Machine Learning. (PMLR) pp.1126–1135.Microtome Publishing Brookline MA(2017)
4. Sung F. Yang Y. Zhang L. Xiang T. Torr P.H. Hospedales T.M.:Learning to compare: Relation network for few‐shot learning. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.1199–1208.IEEE Piscataway NJ(2018)
5. Li W. Xu J. Huo J. Wang L. Gao Y. Luo J.:Distribution consistency based covariance metric networks for few‐shot learning. In:Proceedings of the Thirty‐Third AAAI Conference on Artificial Intelligence pp.8642–8649.AAAI Press Washington DC(2019)