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تسجيل مستخدم جديدMetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
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Few-Shot Learning (FSL) is a challenging task, i.e., how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then predict novel classes via a nearest neighbor classifier with mean-based prototypes. Nevertheless, due to the data scarcity, the mean-based prototypes are usually biased. In this paper, we diminish the bias by regarding it as a prototype optimization problem. Although the existing meta-optimizers can also be applied for the optimization, they all overlook a crucial gradient bias issue, i.e., the mean-based gradient estimation is also biased on scarce data. Consequently, we regard the gradient itself as meta-knowledge and then propose a novel prototype optimization-based meta-learning framework, called MetaNODE. Specifically, we first regard the mean-based prototypes as initial prototypes, and then model the process of prototype optimization as continuous-time dynamics specified by a Neural Ordinary Differential Equation (Neural ODE). A gradient flow inference network is carefully designed to learn to estimate the continuous gradients for prototype dynamics. Finally, the optimal prototypes can be obtained by solving the Neural ODE using the Runge-Kutta method. Extensive experiments demonstrate that our proposed method obtains superior performance over the previous state-of-the-art methods. Our code will be publicly available upon acceptance.
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Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pre-training based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through
the nearest centroid based meta-learning. However, results show that the fine-tuning step makes very marginal improvements. In this paper, 1) we figure out the key reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning the feature extractor is less meaningful; 2) instead of fine-tuning the feature extractor, we focus on estimating more representative prototypes during meta-learning. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative attribute features as priors. Then, we design a prototype completion network to learn to complete prototypes with these priors. To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments show that our method: (i) can obtain more accurate prototypes; (ii) outperforms state-of-the-art techniques by 2% - 9% in terms of classification accuracy. Our code is available online.
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