Neural Network Homographic Augmentation Training
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Solution Overview
Problem
Training image recognition neural networks is challenging when there are few images of an object or when images are not taken from diverse angles and viewpoints, as existing techniques may lead to poor performance or failure to converge due to insufficient data and excessive data augmentation.
Innovation Solution
The approach involves training a neural network using a first set of images and then progressively training additional neural networks with homographic transformations of these images, varying the augmentation rates and initializing weights from previously computed sets to stabilize the training process, allowing for the generation of a trained ensemble neural network that can recognize objects in visual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data augmentation is applied to increase training data diversity, then the neural network can handle more viewpoints and angles, but the training process may fail to converge or performance deteriorates due to excessive augmentation
Solution Approach 1:
The patent applies dynamic control of homography augmentation by progressively increasing the augmentation rate through multiple training stages. The system transitions from static data augmentation to dynamic adjustment, where the degree of homography transformation is controlled by a schedule that increases complexity over time, allowing the network to adapt gradually rather than being exposed to all augmentation levels simultaneously.
Solution Approach 2:
The system performs preliminary training with low homography rates before introducing higher augmentation levels. This staged approach prepares the network for more complex transformations by first establishing baseline performance with simpler data, then progressively introducing more challenging augmented views in controlled increments.
2Adaptability or versatility
If homographic transformation with high alpha value is applied to increase data diversity, then more viewpoints are covered, but training convergence becomes unstable and performance decreases
Solution Approach 1:
The patent implements dynamic control of the homography alpha parameter by organizing training into multiple stages with progressively increasing alpha values. Rather than applying high alpha values from the start, the system dynamically adjusts the transformation strength based on the training stage, ensuring stability while still achieving comprehensive viewpoint coverage through cumulative exposure.
3Reliability
If multiple neural networks are trained with different homography rates, then ensemble performance improves, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary training of multiple networks with different homography rates in parallel or sequence, then combines them into an ensemble. This preliminary action allows the individual networks to be trained efficiently with their respective augmentation levels before being combined, achieving diverse viewpoint coverage without requiring retraining of the entire ensemble.
Solution Approach 2:
The patent merges multiple individually trained neural networks into a single ensemble system. Each network is trained with different homography augmentation levels, and their predictions are combined to achieve improved recognition accuracy. This merging approach allows the system to benefit from diverse training conditions while maintaining efficient inference through the consolidated ensemble.
Data Source
AI summary
A machine trains a first neural network using a first set of images. Training the first neural network comprises computing a first set of weights for a first set of neurons. The machine, for each of one or more alpha values in order from smallest to largest, trains an additional neural network using an additional set of images. The additional set of images comprises a homographic transformation of the first set of images. The homographic transformation is computed based on the alpha value. Training the additional neural network comprises computing an additional set of weights for an additional set of neurons. The additional set of weights is initialized based on a previously computed set of weights. The machine generates a trained ensemble neural network comprising the first neural network and one or more additional neural networks corresponding to the one or more alpha values.