Heterogeneous Few-Shot Model Rectification With Semantic Mapping

By Han-Jia Ye, De-chuan Zhan, Yuan Jiang, Zhi-Hua Zhou. 2020

36 citations recorded by the source corpus.

There still involve lots of challenges when applying machine learning algorithms in unknown environments, especially those with limited training data. To handle the data insufficiency and make a further step towards robust learning, we adopt the learnware notion Z.-H. Zhou, “Learnware: On the future of machine learning,” Front. Comput. Sci., vol. 10, no. 4 pp. 589–590, 2016 which equips a model with an essential reusable property—the model learned in a related task could be easily adapted to the current data-scarce environment without data sharing. To this end, we propose the REctiFy via heterOgeneous pRedictor Mapping (ReForm) framework enabling the current model to take advantage of a related model from two kinds of heterogeneous environment, i.e., either with different sets of features or labels. By Encoding Meta InformaTion (Emit) of features and labels as the model specification, we utilize an optimal transported semantic mapping to characterize and bridge the environment changes. After fine-tuning over a few labeled examples through a biased regularization objective, the transformed heterogeneous model adapts to the current task efficiently. We apply ReForm over both synthetic and real-world tasks such as few-shot image classification with either learned or pre-defined specifications. Experimental results validate the effectiveness and practical utility of the proposed ReForm framework.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Han-Jia Ye
2. De-chuan Zhan
3. Da-Wei Zhou
4. Wei-Lun Chao
5. Fu-Yun Wang
6. Chi Zhang
7. Guosheng Lin
8. Lu Ming

Citations in the corpus

• Forward Compatible Few-Shot Class-Incremental Learning (2022, methodology): “Metric-based algorithms utilize a pretrained backbone for feature extraction, and employ proper distance metrics between support and query instances [26, 37, 39, 45, 52, 53, 57].”
• Co-Transport for Class-Incremental Learning (2021, neutral): “model fusion [37], domain adaptation [7], object detection [10], model reuse [56, 57], and generative models [2, 4].”
• Few-Shot Incremental Learning with Continually Evolved Classifiers (2021, background): “Few-shot learning aims to learn a model that can classify unseen images when only training from scarce labeled training examples [5,48].”
• Few-Shot Learning With a Strong Teacher (2021, background): “[52], [53], [54] proposed special regularization strategies used during fine-tuning.”