Unidimensional Augmentation Algorithm for Deep Learning
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Solution Overview
Problem
Current automated data augmentation processes for deep learning models require significant computational resources and human expertise, with large search spaces and complex optimization algorithms, making them inefficient and impractical for autonomous solutions.
Innovation Solution
A system employing a random unidimensional augmentation algorithm with a single global augmentation parameter to reduce the search space and minimize computational resources, allowing for autonomous data augmentation without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated data augmentation processes employ joint optimization of augmentation parameters and neural network parameters, then model performance can be improved, but the search space size increases to 10^32 and training time significantly increases
Solution Approach 1:
The patent segments the joint optimization problem into two separate optimization problems: (1) optimizing augmentation parameters independently using a unidimensional search space, and (2) optimizing neural network parameters using standard backpropagation. This segmentation reduces the search space from 10^32 to a manageable size while maintaining model performance.
Solution Approach 2:
The patent transforms the high-dimensional joint optimization problem into a unidimensional search space by parameterizing augmentation transformations using a single parameter θ. This dimensionality reduction allows efficient optimization without sacrificing the ability to explore diverse augmentation strategies.
2Extent of automation
If reinforcement learning is used to optimize augmentation policies on a search space of size 10^32, then automated augmentation can be achieved, but thousands of GPU hours are required to find a solution
Solution Approach 1:
The patent changes the parameterization of augmentation transformations from multiple independent parameters to a single unified parameter θ. This parameter change reduces the search space dimensionality, making automated optimization feasible with minimal computational resources while maintaining full automation capability.
3Use of energy by moving object
If RandAugment reduces the search space from 10^32 to 10^2, then computational cost decreases, but 100 full model training iterations are required to settle on an ideal configuration
Solution Approach 1:
The patent performs preliminary optimization of augmentation parameters independently before neural network training, using a unidimensional search that converges quickly. This preliminary action eliminates the need for extensive hyperparameter tuning during model training, reducing both computational cost and training iterations required.
4Use of energy by moving object
If human expertise is employed to pre-select a sub-grid for RandAugment search, then computational cost is reduced, but the solution requires human intervention and loses autonomous capability
Solution Approach 1:
The patent enables the system to automatically optimize augmentation parameters using a unidimensional search algorithm without requiring human expertise for sub-grid selection. The system serves itself by independently finding optimal parameters, maintaining full autonomous capability while keeping computational costs low.
Data Source
AI summary
Techniques regarding autonomous data augmentation are provided. For example, one or more embodiments described herein can regard a system comprising a memory that can store computer-executable components. The system can also comprise a processor, operably coupled to the memory, that executes the computer-executable components stored in the memory. The computer-executable components can include a data augmentation component that executes a random unidimensional augmentation algorithm to augment a dataset for training a machine learning model via a plurality of augmentation operations. The random unidimensional augmentation algorithm can employ a global augmentation parameter that defines: a distortion magnitude associated with the plurality of augmentation operations, and a number of augmentation operations included in the plurality of augmentation operations.