Foreground Object Transplant for Training Data Diversity
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Solution Overview
Problem
The lack of diversity in training data leads to overfitting in predictive models, causing them to perform poorly on new data sets, and manually labeling objects in varied environments is inefficient and time-consuming.
Innovation Solution
A data augmentation system that includes a segmentation module to isolate objects from backgrounds and a transformation module to generate varied images by modifying object properties and transplanting them into different backgrounds, creating an augmented set of images for training predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manually labeling objects in varied environments is performed to increase training data diversity, then the quality and diversity of training data is improved, but the time consumption and efficiency deteriorate
Solution Approach 1:
The system creates synthetic copies of objects by extracting foreground objects from source images and placing them into different background images. This copying process generates diverse training data without manual labeling, directly resolving the contradiction between data diversity and time consumption
Solution Approach 2:
The system performs automatic foreground object extraction and background replacement without human intervention. The computational system serves itself by automatically generating augmented training data through algorithmic processing, eliminating the need for manual labeling while maintaining data diversity
2Reliability
If the initial set of images is used directly for training without augmentation, then the training process is simple and fast, but the predictive model performs poorly on new unseen data sets due to overfitting
Solution Approach 1:
The system performs preliminary data augmentation by generating synthetic training images before the actual model training process. This preliminary action creates a more diverse training dataset that prevents overfitting, improving model reliability on new data while the added complexity is managed through automated processing
3Quantity of substance
If extensive manual labeling is performed to create diverse training data, then the training data quality is improved, but the productivity and efficiency of data preparation deteriorate
Solution Approach 1:
The system replaces the mechanical process of manual labeling with automated computational processing. Algorithms automatically extract foreground objects, select appropriate backgrounds, and composite new training images, substituting human manual work with computational mechanics that dramatically improves productivity while maintaining data diversity
Data Source
AI summary
Systems and methods for augmenting a data set are provided. An example method may include locating a foreground object disposed within a seed image, identifying an object class corresponding to the foreground object, and, based on the identified object class, determining a target value for an object property of the foreground object. The example method may also include applying a transformation function to transform the foreground object into a transformed object, where the transformation function modifies the object property of the foreground object from having an initial value to having the target value. The example method may further include transplanting the transformed object into a background image so as to produce an augmented image and augmenting an initial set of images with the augmented image so as to produce an augmented set of images for training a predictive model.