CIFDM: Continual and Interactive Feature Distillation for Multi-Label Stream Learning

By Yigong Wang, Zhuoyi Wang, Yu Lin, Latifur R. Khan, Dingcheng Li. 2021

19 citations recorded by the source corpus.

Multi-label learning algorithms have attracted more and more attention as of recent. This is mainly because real-world data is generally associated with multiple and non-exclusive labels, which could correspond to different objects, scenes, actions, and attributes. In this paper, we consider the following challenging multi-label stream scenario: the new labels emerge continuously in the changing environments, and are assigned to the previous data. In this setting, data mining solutions must be able to learn the new concepts and avoid catastrophic forgetting simultaneously. We propose a novel continual and interactive feature distillation-based learning framework (CIFDM), to effectively classify instances with novel labels. We utilize the knowledge from the previous tasks to learn new knowledge to solve the current task. Then, the system compresses historical and novel knowledge and preserves it while waiting for new emerging tasks. CIFDM consists of three components: 1) a knowledge bank that stores the existing feature-level compressed knowledge, and predicts the observed labels so far; 2) a pioneer module that aims to learn and predict new emerged labels based on knowledge bank.; 3) an interactive knowledge compression function which is used to compress and transfer the new knowledge to the bank, and then apply the current compressed knowledge to initialize the label embedding of the pioneer for the next task.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Jie Hao
2. Chenlei Guo
3. Xing Fan
4. Zheng Chen
5. Dingcheng Li
6. Eunah Cho
7. Xiaohu Liu

Citations in the corpus

• Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation (2022, methodology): “After a task is completed, the weights are frozen as one prototype (Wang et al., 2021b; d’Autume et al., 2019; Wang et al., 2021a).”