Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels (1910.05199v4)

Abstract: Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a specific task. Common approaches have taken the form of meta-learning: learning to learn on the new problem given the old. Following the recognition that meta-learning is implementing learning in a multi-level model, we present a Bayesian treatment for the meta-learning inner loop through the use of deep kernels. As a result we can learn a kernel that transfers to new tasks; we call this Deep Kernel Transfer (DKT). This approach has many advantages: is straightforward to implement as a single optimizer, provides uncertainty quantification, and does not require estimation of task-specific parameters. We empirically demonstrate that DKT outperforms several state-of-the-art algorithms in few-shot classification, and is the state of the art for cross-domain adaptation and regression. We conclude that complex meta-learning routines can be replaced by a simpler Bayesian model without loss of accuracy.

Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

The paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" presents a novel approach named Deep Kernel Transfer (DKT) for tackling the few-shot learning problem. Few-shot learning is an environment where a model is required to learn from only a small number of labeled examples. Traditional machine learning methods, particularly deep learning, require large datasets to generalize well, making few-shot tasks challenging. This paper addresses these challenges by introducing a Bayesian framework for meta-learning utilizing deep kernels, which allow knowledge transfer across tasks.

Methodology

DKT leverages deep kernel learning to facilitate a Bayesian treatment of the meta-learning inner loop. Deep kernels combine the representational power of neural networks with the flexibility of kernel methods to create scalable covariance functions. This method diverges from conventional meta-learning techniques which typically require complex inner-loop optimization procedures, often destabilizing during training due to joint optimization of task-specific and common parameters.

Instead, DKT models the task-specific parameter optimization as a Bayesian inference problem, effectively integrating out the need for explicit task-specific parameters using Gaussian processes. The primary contributions of DKT in this context include:

1. Simplification: Bypassing the need for task-specific parameter optimization, simplifying the meta-learning process.
2. Uncertainty Estimation: Providing a measure of uncertainty, critical in low-data regimes typical of few-shot learning.
3. Flexibility and Robustness: Applicability to various tasks such as regression, classification, and cross-domain adaptation with high reliability.

DKT implements a maximum likelihood type II (ML-II) approach to learn a set of common parameters and hyperparameters from all tasks, thereby maximizing the marginal likelihood and providing a hierarchical Bayesian model to effectively handle new tasks.

Experimental Results

The empirical evaluation demonstrates that DKT outperforms state-of-the-art few-shot learning methods in classification, regression, and cross-domain scenarios. It shows superior performance particularly when predicting unknown periodic functions and estimating head pose trajectories. In classification tasks on challenging datasets like CUB and mini-ImageNet, DKT reports higher accuracy compared to conventional methods, including MAML and Prototypical Networks.

Theoretical and Practical Implications

The paper's findings suggest that the implementation of meta-learning as a hierarchical Bayesian model, instead of relying on complex optimization routines, can effectively streamline the few-shot learning process without sacrificing accuracy. The framework's capability to quantify uncertainty further enhances its applicability, making it suitable for decision-making contexts where risk assessment is crucial.

Practically, the method simplifies the deployment of few-shot learning solutions, potentially benefiting areas such as medical diagnosis and any domain with constrained data availability. Theoretical implications stress the importance of Bayesian reasoning in artificial intelligence, particularly in learning environments with limited information.

Future Directions

This research opens avenues for further exploration, particularly in integrating DKT with other advanced meta-learning techniques and investigating its applicability in even tougher scenarios, like few-shot continual learning. There is potential for refining the deep kernels used, potentially enhancing their representational capacity and adaptability to diverse datasets.

In conclusion, DKT offers a valuable contribution to the field of few-shot learning, proposing a model that balances simplicity, efficiency, and performance, backed by rigorous Bayesian principles. Its success emphasizes the potential for Bayesian treatments in expanding the capacity of meta-learning frameworks for handling complex real-world tasks.