Incremental Few-Shot Learning via Implanting and Compressing

• URL: http://arxiv.org/abs/2203.10297v1
• Date: Sat, 19 Mar 2022 11:04:43 GMT
• Title: Incremental Few-Shot Learning via Implanting and Compressing
• Authors: Yiting Li, Haiyue Zhu, Xijia Feng, Zilong Cheng, Jun Ma, Cheng Xiang, Prahlad Vadakkepat, Tong Heng Lee
• Abstract summary: Incremental Few-Shot Learning requires a model to continually learn novel classes from only a few examples. We propose a two-step learning strategy referred to as textbfImplanting and textbfCompressing. Specifically, in the textbfImplanting step, we propose to mimic the data distribution of novel classes with the assistance of data-abundant base set. In the textbf step, we adapt the feature extractor to precisely represent each novel class for enhancing intra-class compactness.
• Score: 13.122771115838523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work focuses on tackling the challenging but realistic visual task of Incremental Few-Shot Learning (IFSL), which requires a model to continually learn novel classes from only a few examples while not forgetting the base classes on which it was pre-trained. Our study reveals that the challenges of IFSL lie in both inter-class separation and novel-class representation. Dur to intra-class variation, a novel class may implicitly leverage the knowledge from multiple base classes to construct its feature representation. Hence, simply reusing the pre-trained embedding space could lead to a scattered feature distribution and result in category confusion. To address such issues, we propose a two-step learning strategy referred to as \textbf{Im}planting and \textbf{Co}mpressing (\textbf{IMCO}), which optimizes both feature space partition and novel class reconstruction in a systematic manner. Specifically, in the \textbf{Implanting} step, we propose to mimic the data distribution of novel classes with the assistance of data-abundant base set, so that a model could learn semantically-rich features that are beneficial for discriminating between the base and other unseen classes. In the \textbf{Compressing} step, we adapt the feature extractor to precisely represent each novel class for enhancing intra-class compactness, together with a regularized parameter updating rule for preventing aggressive model updating. Finally, we demonstrate that IMCO outperforms competing baselines with a significant margin, both in image classification task and more challenging object detection task.

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