Meta-Learning Task Generation via Distance Distribution Control
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Solution Overview
Problem
Current meta-learning methods face challenges in creating datasets for few-shot learning tasks, particularly in custom keyword spotting for speech recognition, where tasks are not straightforward to build, leading to varying performance due to different sample arrangements, and existing solutions often assume tasks are provided or easily inferred.
Innovation Solution
A method is proposed to generate tasks by modeling the intrinsic difficulty of each task using the probability distribution of distances between samples in the input data domain, employing nonparametric estimators like kernel density estimation to create tasks with a controlled degree of difficulty, ensuring that training and testing tasks have similar distributions, thereby mitigating meta-overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tasks are built with different sample arrangements in meta-learning, then the model can be trained on diverse task structures, but the performance varies significantly due to inconsistent task difficulty levels
Solution Approach 1:
The patent applies parameter changes by controlling the distance distribution parameter of samples in task generation. By adjusting the distance parameter to match a reference distribution, the method standardizes task difficulty while maintaining structural diversity, thus resolving the contradiction between adaptability and reliability
Solution Approach 2:
The patent employs preliminary action by pre-establishing a reference distance distribution from available data before generating tasks. This reference distribution serves as a template to guide subsequent task creation, ensuring consistent difficulty levels across all generated tasks while allowing structural variation
2Adaptability or versatility
If meta-learning algorithms use multiple tasks with varying difficulty levels, then the model learns from diverse scenarios, but the meta-training process fails to converge properly and generalization is hindered
Solution Approach 1:
The patent controls the distance parameter of samples during task generation to align with a reference distribution. This parameter control ensures that all generated tasks have comparable difficulty levels, enabling proper convergence during meta-training while still providing diverse learning scenarios through different task configurations
Solution Approach 2:
The patent creates equipotentiality in task difficulty by using the reference distance distribution as a standard. All generated tasks are calibrated to have similar difficulty characteristics, creating a level playing field that allows the meta-learning algorithm to converge properly without being biased by varying task difficulties
3Productivity
If tasks are generated without controlling sample distance distribution, then the task creation process is simple and fast, but the resulting tasks have inconsistent difficulty levels leading to meta-overfitting
Solution Approach 1:
The patent performs preliminary action by pre-computing the reference distance distribution from available data before task generation. This reference model is then reused during task creation, allowing the method to maintain high productivity while ensuring consistent task difficulty through the pre-established distribution template
Solution Approach 2:
The patent controls the distance parameter during sample selection in task generation. By adjusting and constraining this parameter to match the reference distribution, the method achieves both efficient task generation and consistent difficulty levels, preventing meta-overfitting while maintaining productivity
Data Source
AI summary
A method for generating meta-learning tasks in order to solve few-shot learning classification. The method enables the homogenization of the difficulty level of each task, so the meta-learning process better converges, and the resulting meta-model better generalizes. For each task, the method controls the distances of the data instances within a given class in the input feature domain based on a reference distribution obtained from a known dataset.