Deep Feature Adversarial Neural Network for Mobile Image Processing
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Solution Overview
Problem
Complex image manipulation techniques, such as neural network-based image domain transfer, are computationally intensive and impractical for implementation on mobile devices due to high processing times, making them unsuitable for real-time applications on low-resource computers like smartphones, especially when applied to live video.
Innovation Solution
A deep-feature adversarial system is implemented on user devices using an adversarially trained transformation subnetwork operating between encoder and decoder layers, trained in two stages: first, the generator network is trained to transform raw object features into deep-feature representations, and second, a transformation subnetwork applies effects to input data, preventing the generation of adversarial examples with noticeable artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex image manipulation techniques like neural network-based image domain transfer are implemented, then image manipulation quality is improved, but processing time increases making them unsuitable for mobile devices
Solution Approach 1:
The neural network is divided into three distinct components: an encoder network that extracts features from input images, a transformation subnetwork that applies domain transfer transformations, and a decoder network that reconstructs the output image. This segmentation allows each component to be optimized independently and processed efficiently on mobile devices with limited computational resources.
Solution Approach 2:
The encoder network is trained in advance to extract meaningful features from input images before the actual domain transfer is needed. These pre-extracted features are then transformed by the transformation subnetwork, avoiding the need to process raw pixels during real-time operation and significantly reducing processing time on mobile devices.
2Power
If server support is used for complex image processing, then processing power is improved, but device independence and portability are reduced
Solution Approach 1:
The mobile device is equipped with the complete domain transfer system including the encoder, transformation subnetwork, and decoder, allowing it to perform image domain transfer operations independently without requiring external server support. The device processes images locally using its own computational resources, enabling offline operation and eliminating network dependencies.
3Adaptability or versatility
If traditional neural networks are used for image domain transfer, then transformation capability is improved, but computational complexity increases making real-time processing impossible
Solution Approach 1:
The transformation capability is segmented into specialized subnetworks: the encoder handles feature extraction, the transformation subnetwork handles domain-specific transformations, and the decoder handles image reconstruction. This segmentation reduces the computational complexity of each individual component while maintaining overall transformation capability.
Solution Approach 2:
Feature extraction is performed in advance by the encoder network, creating a compressed representation that captures essential image characteristics. This preliminary action reduces the dimensionality and complexity of data that needs to be processed during real-time domain transfer, enabling mobile devices to handle transformations efficiently.
Data Source
AI summary
A data item is identified on a device. A neural network that includes an adversarial transformation subnetwork is applied to the data item to generate a modified data item. Output indicative of the modified data item is caused to be presented on the device. The neural network further comprises an encoder and a decoder. The neural network is trained in at least two stages. At least one of the encoder and the decoder is trained in a first stage and the adversarial transformation subnetwork is trained in a second stage.