INCREMENTAL FEW-SHOT LEARNING VIA VECTOR QUANTIZATION IN DEEP EMBEDDED SPACE

Abstract

The capability of incrementally learning new tasks without forgetting old ones is a challenging problem due to catastrophic forgetting. This challenge becomes greater when novel tasks contain very few labelled training samples. Currently, most methods are dedicated to class-incremental learning and rely on sufficient training data to learn additional weights for newly added classes. Those methods cannot be easily extended to incremental regression tasks and could suffer from severe overfitting when learning few-shot novel tasks. In this study, we propose a nonparametric method in deep embedded space to tackle incremental few-shot learning problems. The knowledge about the learned tasks is compressed into a small number of quantized reference vectors. The proposed method learns new tasks sequentially by adding more reference vectors to the model using few-shot samples in each novel task. For classification problems, we employ the nearest neighbor scheme to make classification on sparsely available data and incorporate intra-class variation, less forgetting regularization and calibration of reference vectors to mitigate catastrophic forgetting. In addition, the proposed learning vector quantization (LVQ) in deep embedded space can be customized as a kernel smoother to handle incremental few-shot regression tasks. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods in incremental learning.

1. INTRODUCTION

Incremental learning is a learning paradigm that allows the model to continually learn new tasks on novel data, without forgetting how to perform previously learned tasks (Cauwenberghs & Poggio, 2001; Kuzborskij et al., 2013; Mensink et al., 2013) . The capability of incremental learning becomes more important in real-world applications, in which the deployed models are exposed to possible out-of-sample data. Typically, hundreds of thousands of labelled samples in new tasks are required to re-train or fine-tune the model (Rebuffi et al., 2017) . Unfortunately, it is impractical to gather sufficient samples of new tasks in real applications. In contrast, humans can learn new concepts from just one or a few examples, without losing old knowledge. Therefore, it is desirable to develop algorithms to support incremental learning from very few samples. While a natural approach for incremental few-shot learning is to fine-tune part of the base model using novel training data (Donahue et al., 2014; Girshick et al., 2014) , the model could suffer from severe over-fitting on new tasks due to a limited number of training samples. Moreover, simple fine-tuning also leads to significant performance drop on previously learned tasks, termed as catastrophic forgetting (Goodfellow et al., 2014) . Recent attempts to mitigate the catastrophic forgetting are generally categorized into two streams: memory relay of old training samples (Rebuffi et al., 2017; Shin et al., 2017; Kemker & Kanan, 2018) and regularization on important model parameters (Kirkpatrick et al., 2017; Zenke et al., 2017) . However, those incremental learning approaches are developed and tested on unrealistic scenarios where sufficient training samples are available in novel tasks. They may not work well when the training samples in novel tasks are few (Tao et al., 2020b) .

