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20192025
most citedOvercoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima

14 citations · 31 across the 16 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.LG2021★ 14 cited

Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima

Guangyuan Shi, Jiaxin Chen, Wenlong Zhang +2

This paper considers incremental few-shot learning, which requires a model to continually recognize new categories with only a few examples provided. Our study shows that existing…

cs.CL2021

Effectiveness of Pre-training for Few-shot Intent Classification

Haode Zhang, Yuwei Zhang, Li-Ming Zhan +4

This paper investigates the effectiveness of pre-training for few-shot intent classification. While existing paradigms commonly further pre-train language models such as BERT on a…

cs.LG2021

Adaptation-Agnostic Meta-Training

Jiaxin Chen, Li-Ming Zhan, Xiao-Ming Wu +1

Many meta-learning algorithms can be formulated into an interleaved process, in the sense that task-specific predictors are learned during inner-task adaptation and meta-parameters…

cs.CL2021★ 1 cited

Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training

Li-Ming Zhan, Haowen Liang, Bo Liu +3

Out-of-scope intent detection is of practical importance in task-oriented dialogue systems. Since the distribution of outlier utterances is arbitrary and unknown in the training st…

cs.CV2021★ 10 cited

SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering

Bo Liu, Li-Ming Zhan, Li Xu +3

Medical visual question answering (Med-VQA) has tremendous potential in healthcare. However, the development of this technology is hindered by the lacking of publicly-available and…