Meta-SGD: Learning to Learn Quickly for Few-Shot Learning (1707.09835v2)

Abstract: 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.

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

The paper presents Meta-SGD, a novel meta-learning algorithm designed to address the challenge of few-shot learning in both supervised and reinforcement learning contexts. Few-shot learning involves training models to generalize effectively from a limited number of examples, a situation where traditional deep learning methods typically struggle due to their data-hungry nature. Meta-learning strategies, including Meta-SGD, endeavor to harness the shared information from related tasks, thereby enabling faster and more accurate learning with fewer examples.

Key Contributions

Meta-SGD stands out by effectively optimizing and simplifying the meta-learning process. The authors highlight several primary contributions:

1. Unified Meta-Learning Framework: Meta-SGD serves as a generalized SGD-like meta-learner capable of initializing and adapting any differentiable learner. This adaptability positions Meta-SGD to handle various problem domains efficiently.
2. Learning Multiple Aspects: Unlike existing meta-learners such as MAML, which only learn the model initialization, Meta-SGD simultaneously learns three critical components: the learner’s initialization, update direction, and learning rate. This holistic approach enhances Meta-SGD's capacity to perform rapid adaptation to new tasks.
3. Ease of Training: Meta-SGD distinguishes itself from more complex meta-learners like Meta-LSTM by offering a simpler and more efficient training process, making it more accessible for practical applications.

Experimental Validation

The authors conducted extensive empirical evaluations across several benchmarks for few-shot learning:

• Regression: On the $K$-shot regression task involving sine waves, Meta-SGD outperformed MAML significantly. Specifically, with $K=5$, Meta-SGD achieved a mean squared error (MSE) of $0.90 \pm 0.16$ compared to MAML's $1.13 \pm 0.18$.
• Classification: Using datasets like Omniglot and MiniImagenet, Meta-SGD showed superior performance across different few-shot scenarios. For instance, in the 1-shot 5-way classification on MiniImagenet, Meta-SGD achieved an accuracy of $50.47 \pm 1.87 \%$, surpassing the performance of MAML, Matching Nets, and Meta-LSTM.
• Reinforcement Learning: For 2D navigation tasks, Meta-SGD also demonstrated higher returns than MAML. On tasks with fixed start positions, Meta-SGD attained an average return of $-8.64 \pm 0.68$ compared to MAML's $-9.12 \pm 0.66$.

Implications and Future Work

The theoretical and practical implications of Meta-SGD are vast:

• Efficient Learning: Being able to learn effectively with minimal data can significantly reduce the cost and time associated with data collection and model training, particularly in environments where data is scarce or expensive to obtain.
• Rapid Adaptation: In dynamic environments such as robotics or autonomous systems, Meta-SGD’s capability for rapid adaptation can be crucial, enabling real-time learning and decision-making.

Regarding future developments, several directions are worthy of exploration:

1. Scalability to Large-Scale Meta-Learning: Extending the approach of Meta-SGD to handle large-scale datasets and more complex learners remains a crucial challenge. Efficient optimization and resource management strategies will be essential to make large-scale meta-learning feasible.
2. Versatility in Unseen Situations: Evaluating and enhancing the ability of Meta-SGD to generalize across very diverse tasks and event domains will be important for broadening its applicability. This includes addressing multi-tasking and cross-domain learning scenarios.
3. Continual Learning: Incorporating mechanisms for continual learning can further enhance the practical value of Meta-SGD by enabling models to adapt continuously in lifelong learning situations.

In summary, Meta-SGD represents a significant advancement in the field of meta-learning by improving the efficiency and capability of few-shot learning through a simplified yet powerful framework. The results and implications outlined in the paper underscore the potential of Meta-SGD to facilitate new applications and advancements in machine learning and AI.