[Paper Review] Imitation networks: Few-shot learning of neural networks from scratch

Akisato Kimura, Zoubin Ghahramani+3 more|arXiv (Cornell University)|Feb 8, 2018

Domain Adaptation and Few-Shot Learning39 references13 citations

TL;DR

This paper introduces imitation networks, a few-shot learning method that trains neural networks from scratch using only a small amount of labeled data by mimicking robust reference estimators. By optimizing pseudo training examples as model parameters, the approach achieves superior performance over standard knowledge distillation and naive training, even with minimal labeled data.

ABSTRACT

In this paper, we propose imitation networks, a simple but effective method for training neural networks with a limited amount of training data. Our approach inherits the idea of knowledge distillation that transfers knowledge from a deep or wide reference model to a shallow or narrow target model. The proposed method employs this idea to mimic predictions of reference estimators that are much more robust against overfitting than the network we want to train. Different from almost all the previous work for knowledge distillation that requires a large amount of labeled training data, the proposed method requires only a small amount of training data. Instead, we introduce pseudo training examples that are optimized as a part of model parameters. Experimental results for several benchmark datasets demonstrate that the proposed method outperformed all the other baselines, such as naive training of the target model and standard knowledge distillation.

Motivation & Objective

• To address the challenge of training deep neural networks with limited labeled data, where overfitting is a major issue.
• To develop a method that enables effective knowledge transfer from robust reference models to target models without requiring large labeled datasets.
• To train shallow or narrow target networks from scratch using a small number of real examples and learned pseudo-examples.
• To improve generalization in low-data regimes by leveraging the predictive consistency of reference estimators.

Proposed method

• The method uses knowledge distillation principles but applies them in a few-shot setting with minimal labeled data.
• It introduces pseudo training examples that are optimized as part of the model parameters during training.
• The target network is trained to mimic the predictions of a more robust reference estimator, which is pre-trained on a larger, potentially unrelated dataset.
• The pseudo-examples are differentiable and updated via backpropagation to improve the target model’s generalization.
• The training objective minimizes the cross-entropy loss between the target model’s output and the reference estimator’s output on both real and pseudo-examples.
• The approach enables end-to-end training of the target network from scratch using only a few labeled examples and optimized pseudo-data.

Experimental results

Research questions

• RQ1 Can knowledge distillation be effectively applied in few-shot learning scenarios with minimal labeled data?
• RQ2 Can optimizing pseudo-training examples as model parameters improve generalization in low-data regimes?
• RQ3 How does imitation networks compare to standard knowledge distillation and naive training in terms of accuracy and robustness?
• RQ4 Can a target network be trained from scratch to achieve high performance using only a small number of real examples and pseudo-examples?

Key findings

• Imitation networks outperformed naive training of the target model on all benchmark datasets tested.
• The method achieved better accuracy than standard knowledge distillation, which typically requires large labeled datasets.
• The use of optimized pseudo-examples significantly reduced overfitting in low-data regimes.
• The approach demonstrated strong generalization performance even with only a few labeled examples.
• The reference estimator’s robustness was effectively transferred to the target model, enabling high performance from scratch.
• Experimental results confirmed the method’s superiority across multiple benchmark datasets.