Few-shot learning that trains image classifiers over few labeled examples per category is a challenging task. In this paper, we propose to exploit an additional big dataset with different categories to improve the accuracy of few-shot learning over our target dataset. Our approach is based on the observation that images can be decomposed into objects, which may appear in images from both the additional dataset and our target dataset. We use the object-level relation learned from the additional dataset to infer the similarity of images in our target dataset with unseen categories. Nearest neighbor search is applied to do image classification, which is a non-parametric model and thus does not need fine-tuning. We evaluate our algorithm on two popular datasets, namely Omniglot and MiniImagenet. We obtain 8.5 and 2.7 absolute improvements for 5-way 1-shot and 5-way 5-shot experiments on MiniImagenet, respectively. Source code will be published upon acceptance.
Towards the optimization challenge, meta learning trains a meta learner that guides the optimization algorithms to fine-tune the learner (i.e. classifier). It is also called learning to learn. The meta learner is trained iteratively and learns slowly. For each iteration, an episode is sampled from the training dataset, which has the same setting as the test scenario. In other words, an episode has the same number of categories and the same number of examples per category as the test. The meta learner is trained to fine-tune the classifier for a large number of episodes. After training, the meta learner is expected to guide the learning of basic learner, e.g. the classifier for the test episodes. The meta learner of MAML @cite_1 learns good parameter initialization such that the fine-tuning can adapt the parameters quickly and effectively for the test episodes. Meta-SGD's meta learner @cite_0 generates both the initialization and learning rate for the fine-tuning optimization algorithm. A more aggressive approach @cite_2 is to learn a LSTM to generate the updates for fine-tuning. It replaces the optimization of basic learner with LSTM for fine-tuning.
@cite_0 @cite_1 @cite_2
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Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial. In this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner that can initialize and adapt any differentiable learner in just one step, on both supervised learning and reinforcement learning. Compared to the popular meta-learner LSTM, Meta-SGD is conceptually simpler, easier to implement, and can be learned more efficiently. Compared to the latest meta-learner MAML, Meta-SGD has a much higher capacity by learning to learn not just the learner initialization, but also the learner update direction and learning rate, all in a single meta-learning process. Meta-SGD shows highly competitive performance for few-shot learning on regression, classification, and reinforcement learning. Though deep neural networks have shown great success in the large data domain, they generally perform poorly on few-shot learning tasks, where a model has to quickly generalize after seeing very few examples from each class. The general belief is that gradient-based optimization in high capacity models requires many iterative steps over many examples to perform well. Here, we propose an LSTM-based meta-learner model to learn the exact optimization algorithm used to train another learner neural network in the few-shot regime. The parametrization of our model allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while also learning a general initialization of the learner network that allows for quick convergence of training. We demonstrate that this meta-learning model is competitive with deep metric-learning techniques for few-shot learning.