Task-Agnostic Meta-Learning for Few-shot Learning (1805.07722v1)

Abstract: Meta-learning approaches have been proposed to tackle the few-shot learning problem.Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks. However, the generalizability on new tasks of a meta-learner could be fragile when it is over-trained on existing tasks during meta-training phase. In other words, the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks, especially when only very few examples are available to update the model. To avoid a biased meta-learner and improve its generalizability, we propose a novel paradigm of Task-Agnostic Meta-Learning (TAML) algorithms. Specifically, we present an entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks. Alternatively, a more general inequality-minimization TAML is presented for more ubiquitous scenarios by directly minimizing the inequality of initial losses beyond the classification tasks wherever a suitable loss can be defined.Experiments on benchmarked datasets demonstrate that the proposed approaches outperform compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks.

Task-Agnostic Meta-Learning for Few-Shot Learning

The paper "Task-Agnostic Meta-Learning for Few-shot Learning" introduces a novel approach to address challenges faced by meta-learning algorithms in the few-shot learning paradigm. The authors present methods to improve the generalization capabilities of meta-learners by proposing Task-Agnostic Meta-Learning (TAML) algorithms.

Overview

Meta-learning or "learning to learn" has proven effective for few-shot learning by leveraging prior experiences across tasks. Current meta-learning models, however, risk overfitting to training tasks, which can impair their adaptability to new tasks with significant deviations. To mitigate this, the paper proposes TAML, characterized by two key approaches: entropy-maximization and inequality-minimization.

Entropy-Based TAML

The entropy-based TAML approach involves meta-learning an initial model that maintains high uncertainty across output labels, thus avoiding predisposition towards any given task. By increasing the entropy of predicted labels before model adaptation, this method effectively retains task agnosticism. The entropy-reduction mechanism ensures model confidence is selectively enhanced following adaptation, allowing the model to emerge as task-specific as necessary without inherent bias.

Inequality-Minimization TAML

This approach extends the concept of task-agnosticism across broader contexts beyond classification by minimizing performance inequality across tasks. The authors borrow from economic inequality measures—such as Theil Index and Generalized Entropy Index—to minimize the performance loss disparities across tasks during training. This method positions the TAML paradigm as more universally applicable, especially to non-classification problems like regression and reinforcement learning.

Results

Experimental results on benchmark datasets like Omniglot and Mini-Imagenet demonstrate that TAML strategies notably outperform existing meta-learning algorithms such as MAML and Meta-SGD in few-shot classification settings. The authors compare the approaches on architectures with and without convolutional layers and highlight TAML's superior performance, particularly in 1-shot learning contexts.

In addition, TAML shows substantial improvements in reinforcement learning settings, such as the 2D navigation task, where TAML configurations outperform MAML after multiple gradient steps. This establishes TAML’s robustness across different learning paradigms.

Implications and Future Work

The introduction of TAML algorithms holds several theoretical and practical implications. By establishing a task-agnostic meta-learning paradigm, models are less reliant on the task distribution observed during training, enhancing their applicability in diverse scenarios. Practically, this method could reduce the data and computational requirements for adapting to new tasks, an advantage in fast-paced or resource-constrained environments.

Potential future research directions include the exploration of TAML in various non-stationary environments or domains with significant class imbalance. Investigating more nuanced inequality measures that align closely with domain-specific performance criteria could also refine the approach.

Overall, TAML represents a significant progression in the meta-learning field, particularly in its utility for developing adaptable artificial intelligence that approaches the flexibility of human learning. Future work may further delve into embedding TAML within larger, more complex systems to harness its full potential across a broader spectrum of AI applications.