FP AINET: FUSION PROTOTYPE WITH ADAPTIVE INDUCTION NETWORK FOR FEW-SHOT LEARNING Anonymous authors Paper under double-blind review

Abstract

A prototypical network treats all samples equally and does not consider the noisy samples, which leads to a biased class representation. In this paper, we propose a novel fusion prototype with an adaptive induction network (FP AINet) for fewshot learning that can learn representative prototypes from a few support samples. Specifically, to address the problem of noisy samples, an adaptive induction network is developed, which can learn different class representations for queries and assign adaptive scores for support samples according to their relative significance. Moreover, FP AINet can generate a more accurate prototype than comparison methods by considering the query-related samples. With an increasing number of samples, the prototypical network is more expressive since the adaptive induction network ignores the relative local features. As a result, a Gaussian fusion algorithm is designed to learn more representative prototypes. Extensive experiments are conducted on three datasets: miniImageNet, tieredImageNet, and CIFAR FS. The experimental results compared with the state-of-the-art few-shot learning methods demonstrate the superiority of FP AINet.

1. INTRODUCTION

Few-shot learning aims to learn classifiers for novel classes with limited data. Prototypical network (PN) (Snell et al. (2017) ) averages the support features as the prototype. While most of the previous research has achieved promising results, those methods generally assume that the samples used for training were carefully selected to represent their class. The expected prototype should have the smallest distance from all other samples in its class (Liu et al. (2020) ), and each sample significantly contributes to the final performance when training from a few labeled samples. Unfortunately, the existing dataset frequently contains mislabeled samples because of weakly automated supervised annotation, ambiguity, or human error (Liang et al. (2022) ). In addition, since some images have multiple objects and unrelated background information, the accuracy can be affected by a single noisy example. As illustrated in Figure 1 2018)). Optimization-based methods readily learn the model's parameters to adapt to each task using gradient descent. However, these methods need to be fine-tuned for the target tasks. Metric-based methods are more efficient and applicable than optimization-based methods. Metricbased methods learn a good metric to calculate the similarity between query and the support samples using a pre-defined distance function, such as cosine similarity (Vinyals et al. 2021)), but since it is easy to introduce sample noise or class differences, a novel method of fusion prototype with an adaptive induction network (FP AINet) is proposed to solve the issue. The induction network (Geng et al. ( 2019)) designs a non-linear mapping from sample vector to class vector to diminish the



(a), the PN is easily affected by noisy samples. Metalearning approaches have become the dominant paradigm for few-shot learning (Chen et al. (2020); Tian et al. (2020); Yao et al. (2021)). Meta-learning approaches can be roughly summarized into two categories: optimization-based methods (Antoniou et al. (2019); Kao et al. (2022)) and metric-based methods (Vinyals et al. (2016); Sung et al. (

(2016)), euclidean distance (Snell et al. (2017); Koch et al. (2015)), earth mover's distance (Zhang et al. (2020)), or a distance parameterized by a neural network (Sung et al. (2018); Zhang et al. (2018)), which has achieved remarkable success due to its fewer parameters. To obtain more representative prototypes, many methods correct the prototype by using similar samples (Yang et al. (2021); Liu et al. (2020)) or additional knowledge (Zhang et al. (

