Generative Data Augmentation for Rare ML Scenarios
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Solution Overview
Problem
Existing machine learning models, such as deep neural networks, face challenges in achieving robust and generalized performance due to the rarity of certain scenarios in training data, leading to poor accuracy in underrepresented scenarios, despite the use of data augmentation techniques like rotation and flipping.
Innovation Solution
The use of a generative neural network to generate additional training data with specific attributes, allowing for the augmentation of existing datasets to address performance gaps in machine learning models, ensuring sufficient representation of underrepresented scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data collection methods are used to gather more training data, then the quantity of training data increases, but the cost, time, and labor resources required increase significantly
Solution Approach 1:
The patent uses a generative neural network to create synthetic copies of training data that mimic real-world scenarios. Instead of collecting actual data through labor-intensive fieldwork, the system generates realistic training samples computationally, dramatically reducing the time and resources needed while maintaining data quantity and quality
Solution Approach 2:
The patent replaces the mechanical process of physical data collection (human labor, fieldwork, sensors) with a computational system. The generative neural network substitutes physical data gathering mechanisms with algorithmic data synthesis, eliminating the need for costly and time-consuming real-world data collection while producing sufficient training data
2Adaptability or versatility
If conventional data augmentation techniques (rotation, flipping, cropping) are applied to existing data, then some variations are addressed, but underrepresented scenarios remain poorly represented
Solution Approach 1:
The patent changes the fundamental parameters of data augmentation by moving from simple geometric transformations to generative model-based synthesis. The system adjusts attributes like object presence, background conditions, lighting, and scenario composition to create diverse training samples that specifically target underrepresented scenarios, thereby improving both adaptability and reliability
Solution Approach 2:
The patent performs preliminary analysis to identify underrepresented scenarios before generating augmented data. By proactively detecting which scenarios lack sufficient representation and then generating targeted synthetic data for those specific cases, the system ensures comprehensive coverage and improves reliability across all scenarios rather than relying on random transformations
3Adaptability or versatility
If more real-world data is collected to cover rare scenarios, then scenario coverage improves, but the cost and complexity of data collection increase
Solution Approach 1:
The patent creates synthetic copies of rare scenario data through the generative neural network, eliminating the need to physically collect and manage complex real-world data for every possible scenario. This approach maintains comprehensive scenario coverage while avoiding the complexity of building and maintaining extensive data collection infrastructure
Data Source
AI summary
A machine learning model (MLM) may be trained and evaluated. Attribute-based performance metrics may be analyzed to identify attributes for which the MLM is performing below a threshold when each are present in a sample. A generative neural network (GNN) may be used to generate samples including compositions of the attributes, and the samples may be used to augment the data used to train the MLM. This may be repeated until one or more criteria are satisfied. In various examples, a temporal sequence of data items, such as frames of a video, may be generated which may form samples of the data set. Sets of attribute values may be determined based on one or more temporal scenarios to be represented in the data set, and one or more GNNs may be used to generate the sequence to depict information corresponding to the attribute values.