Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (1903.03096v4)

Abstract: Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks. We experiment with popular baselines and meta-learners on Meta-Dataset, along with a competitive method that we propose. We analyze performance as a function of various characteristics of test tasks and examine the models' ability to leverage diverse training sources for improving their generalization. We also propose a new set of baselines for quantifying the benefit of meta-learning in Meta-Dataset. Our extensive experimentation has uncovered important research challenges and we hope to inspire work in these directions.

• The paper presents a comprehensive benchmark integrating ten datasets to rigorously evaluate few-shot learning models.
• It employs hierarchical class sampling to mirror natural variability and generate realistic task challenges.
• Experiments reveal that training on heterogeneous data enhances model generalization, with performance varying across task conditions.

Overview of Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

The paper introduces Meta-Dataset, a novel and comprehensive benchmark designed for evaluating few-shot learning systems. Few-shot learning aims to facilitate learning tasks with minimal examples, emulating human-like learning flexibility. While existing datasets such as Omniglot and mini-ImageNet serve as standard benchmarks, their constrained complexity and limited scope prompt the need for a more diverse and realistic challenge. Meta-Dataset addresses these limitations by offering a large-scale, multi-dataset benchmark that encompasses varied data distributions and real-world complexities.

Key Contributions

1. Benchmark Design: Meta-Dataset integrates ten distinct datasets to facilitate few-shot learning evaluation across diverse visual domains. This approach enables analysis of model generalization capabilities not just within, but also across various datasets.
2. Realistic Task Generation: It employs hierarchical and structurally-aware class sampling, particularly leveraging the class hierarchies in ImageNet and Omniglot, to produce tasks that mirror natural variability in class and instance distribution.
3. Evaluation of Models: The paper evaluates common meta-learning models like Meta-Learners and Prototypical Networks, alongside a newly proposed Proto-MAML, showcasing their performance across heterogeneous datasets.
4. Analysis of Training Across Diverse Sources: The experiments investigate whether training models on this varied dataset ensemble improves generalization over single-origin training, revealing challenges in exploiting heterogeneous data sources.

Experimental Insights

The experiments reveal notable observations:

• Model Performance: Proto-MAML emerges as a top performer, demonstrating robustness and adaptability across the tasks in Meta-Dataset. However, the results indicate varying performance gains across datasets, emphasizing model-specific strengths and weaknesses.
• Training Source Impact: Training on the full array of datasets can enhance performance on certain test datasets, particularly those distinct from ImageNet-like distributions, such as Quick Draw and Omniglot. Nevertheless, performance benefits are not uniformly observed, indicating complexities in transferring knowledge across heterogeneous data.
• Way and Shot Effects: The results confirm that increasing the number of ways (i.e., classes) in a task corresponds to an increase in difficulty, while more shots (i.e., examples per class) improve model accuracy. Different models benefit unevenly from additional data, underlining the importance of designing adaptable algorithms for varying test conditions.

Practical and Theoretical Implications

Meta-Dataset presents substantial theoretical and practical implications for the advancement of few-shot learning:

• Generalization: The diverse and hierarchical nature of Meta-Dataset prompts the need for models capable of handling a wide array of class distributions and task specifications, thus pushing research towards creating more universally adaptable learning algorithms.
• Benchmarking Standards: By setting a new standard for few-shot learning evaluation, Meta-Dataset encourages the development of benchmarks that realistically emulate complex, real-world learning scenarios, refining the evaluation of AI systems.
• Future Research: The challenges identified in leveraging heterogeneous datasets for improved generalization suggest that future research needs to focus on dataset-aware model training strategies and adaptive learning techniques.

In conclusion, Meta-Dataset significantly advances the evaluation framework for few-shot learning, catalyzing efforts towards developing more capable and flexible AI systems. Its introduction serves both as a critical tool for assessment and as a catalyst for future methodological innovations in the field.