Dataset Optimization via Gradient Flows for Transfer Learning
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Solution Overview
Problem
Traditional machine learning (ML) practices are model-centric, assuming fixed data distributions and failing to effectively capture data manipulation and novel data-centric problems such as transfer learning or dataset synthesis, which limits their ability to optimize datasets for improved model performance across different data distributions.
Innovation Solution
The approach involves flowing a first dataset towards a target dataset based on a specified objective using gradient flows and optimal transport distances, allowing for dataset modification and optimization rather than modifying model parameters, thereby enabling efficient dataset interpolation, synthesis, and aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional model-centric ML practices are used to adjust model parameters, then model performance can be optimized, but the ability to effectively manipulate and optimize datasets for transfer learning and dataset synthesis is limited
Solution Approach 1:
The patent inverts the traditional model-centric approach by making the dataset the variable to be optimized rather than the model parameters. Instead of adjusting model parameters to fit fixed data, the system flows the dataset towards a target distribution using gradient flows, enabling data-centric solutions for transfer learning and dataset synthesis
Solution Approach 2:
The patent changes the optimization parameters from model parameters to dataset parameters. By representing datasets as probability distributions and optimizing these distributions through gradient flows in the space of probability measures, the system enables flexible manipulation of data characteristics while maintaining a relatively simple framework
2Ease of operation
If datasets are kept fixed as in traditional ML, then model training is straightforward, but the ability to perform data manipulation and augmentation is insufficient
Solution Approach 1:
The patent introduces dynamics to the previously static dataset by enabling continuous transformation of data distributions through gradient flows. The dataset can dynamically adapt and flow towards target distributions, providing flexible data manipulation capabilities while maintaining reliable model performance through controlled transformation processes
3Measurement precision
If large datasets are used to improve model accuracy, then model performance improves, but the requirement for extensive data collection and annotation increases
Solution Approach 1:
The patent creates optimized copies of datasets by flowing data towards target distributions. Instead of collecting and annotating large amounts of raw data, the system generates synthetic data copies that capture the essential characteristics and statistical properties of the target distribution, achieving high model accuracy with reduced data requirements
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for machine learning (ML) by flowing a dataset towards a target dataset. A method can include receiving a request to operate on a first dataset including first feature, label pairs, identifying a second dataset from multiple datasets, the second dataset including second feature, label pairs, determining a distance between the first feature, label and the second feature, label pairs, and flowing the first dataset using a dataset objective that operates based on the determined distance to generate an optimized dataset.