Class-Conditioned Image Augmentation for Label-Efficient Classifiers
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Solution Overview
Problem
The high cost and labor-intensive process of labeling training images for image classifiers, along with the risk of learning confounding correlations and class imbalance, hinders effective classification accuracy.
Innovation Solution
A method using variational autoencoders and generative adversarial networks to generate training images that condition on target classes and latent representations, breaking confounding correlations and addressing class imbalance, while optimizing image generation to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of training images is used, then classification accuracy can be improved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent uses a generator network to create synthetic training images that copy the essential features and characteristics of real images. These generated images serve as substitutes for manually labeled images, providing the training data needed for classification models without requiring actual manual labeling of physical images.
Solution Approach 2:
The system employs an autoencoder-network to automatically generate diverse training images from a small set of manually labeled seed images. The network self-trains by encoding the seed images into latent representations and then decoding them to produce varied training samples, eliminating the need for extensive manual labeling while maintaining data quality.
2Reliability
If diverse training images are collected to improve classification robustness, then the model generalization improves, but the data collection and labeling cost increases
Solution Approach 1:
The patent transforms a small set of seed images into diverse training images by manipulating parameters in the latent space of the autoencoder-network. By varying the latent representations and applying different decoding operations, the system generates images with diverse characteristics (different orientations, lighting conditions, backgrounds) from the same source material, achieving data diversity without additional collection costs.
Solution Approach 2:
The system moves from the image space to the latent space through the autoencoder-network, creating a compressed representation of the input images. In this lower-dimensional latent space, diverse training images are generated by varying the latent vectors, then mapped back to image space. This dimensional transformation enables efficient generation of diverse images without proportionally increasing the number of source images needed.
3Measurement precision
If the training data set is enlarged with more images, then classification accuracy improves, but the manual labeling effort increases proportionally
Solution Approach 1:
The patent segments the training data generation process into two distinct phases: (1) manual labeling of a small seed set of images, and (2) automated generation of the remaining training data through the autoencoder-network. This segmentation allows the expensive manual labeling operation to be performed only on a minimal subset, while the bulk of the training data is produced automatically, dramatically improving labeling efficiency.
Solution Approach 2:
The system performs preliminary action by manually labeling only a small seed set of images before training the autoencoder-network. This preliminary labeled data is then used to train the generator, which subsequently produces the majority of the training dataset automatically. This preliminary action approach eliminates the need for continuous manual labeling throughout the entire data collection process.
Data Source
AI summary
A method for generating, from an input image, an output image that a given image classifier classifies into a target class chosen from multiple available classes of a given classification. The method includes mapping, using a trained encoder network, the input image to a lower dimensional representation in a latent space; drawing a noise sample from a given distribution; and mapping, using a trained generator network, the noise sample to an output image, wherein this mapping is conditioned both on the target class and on the representation.