Neural Network Control via Intermediate Latent Space Modification
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Solution Overview
Problem
Conventional generative neural networks offer limited control over image generation, requiring additional data and complex input methods to achieve desired effects, leading to user dissatisfaction.
Innovation Solution
A generative neural network control system that modifies intermediate latent spaces by applying decomposition vectors to activation values, allowing for precise control over image effects such as translation, rotation, and style changes through simple user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional input methods (class vectors or additional images) are used to control image generation, then some level of control is achieved, but the control is limited and user dissatisfaction occurs
Solution Approach 1:
The patent transitions from controlling only the initial layer input to controlling intermediate layers of the neural network. By adding control dimensions at multiple layer levels, the system achieves comprehensive control over various image effects (translation, rotation, scaling, style) without requiring additional training data or complex input methods.
Solution Approach 2:
The patent divides the control mechanism into separate decomposition vectors for different image effects (translation, rotation, scaling, style). Each decomposition vector independently controls a specific effect by modifying activation values at appropriate intermediate layers, allowing users to achieve multiple effects through simple parameter adjustments rather than complex input data.
2Ease of operation
If additional data is provided to improve control, then more control is achieved, but the complexity and difficulty of obtaining data increases
Solution Approach 1:
The system uses the neural network's own internal structure and pre-trained weights to achieve control. By extracting decomposition vectors from the network's intermediate layers and using these to modify activation values, the system achieves precise control without requiring any external training data or additional input images from users.
Solution Approach 2:
The patent changes the control parameters from external data inputs to internal activation value modifications. By adjusting decomposition vectors that operate on activation values at intermediate layers, the system achieves precise control over image effects while avoiding the complexity of data acquisition and preprocessing.
3Productivity
If conventional control methods are used, then the system remains simple, but the productivity and speed of achieving desired effects is reduced
Solution Approach 1:
The patent performs preliminary computation by extracting decomposition vectors from the neural network's intermediate layers during system initialization. These pre-computed decomposition vectors enable rapid control of image effects during operation, as users only need to specify desired effects without triggering complex data processing or additional training procedures.
Data Source
AI summary
A generative neural network control system controls a generative neural network by modifying the intermediate latent space in the generative neural network. The generative neural network includes multiple layers each generating a set of activation values. An initial layer (and optionally additional layers) receives an input latent vector, and a final layer outputs an image generated based on the input latent vector. The data that is input to each layer (other than the initial layer) is referred to as data in an intermediate latent space. The data in the intermediate latent space includes activation values (e.g., generated by the previous layer or modified using various techniques) and optionally a latent vector. The generative neural network control system modifies the intermediate latent space to achieve various different effects when generating a new image.