GAN Latent Space Control With Disentangled Semantic Directions
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Solution Overview
Problem
Existing GAN models require large amounts of labeled data for controlling output semantics, leading to artifacts and attribute entanglements, which degrade user experience in applications like augmented reality.
Innovation Solution
Utilize gradient directions of auxiliary networks to control semantics in GAN latent codes, enabling disentangled controls using minimal labeled data, and select important latent code channels with Grad-CAM masks for improved manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of labelled training data are used to train conditional GANs, then control over output semantics is improved, but data acquisition cost and time increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training an unconditional GAN on unlabeled data to learn the data distribution, then using this pre-trained model to generate synthetic labeled data that serves as training data for conditional GANs. This preliminary step eliminates the need to manually collect and label large datasets, directly resolving the contradiction between semantic control and data acquisition time
Solution Approach 2:
The patent uses the copying principle by generating synthetic images through the pre-trained unconditional GAN that replicate the statistical properties of real images. These synthetic copies are then paired with generated labels to create training datasets, replacing the need to copy actual labeled real-world data, thus reducing data acquisition time while maintaining semantic control
2Ease of operation
If current face-editing GAN methods are used, then semantic control is achieved, but artifacts and attribute entanglements appear reducing user experience
Solution Approach 1:
The patent applies segmentation by disentangling the latent space into independent semantic dimensions using Principal Component Analysis (PCA). Each principal component represents a separate semantic attribute (e.g., smile, age, gender), allowing independent manipulation without affecting other attributes. This segmentation eliminates attribute entanglement and artifacts while maintaining semantic control
Solution Approach 2:
The patent uses parameter changes by transforming the latent code parameters through PCA to identify and manipulate only the relevant semantic dimensions. By changing specific latent parameters corresponding to desired semantic attributes while keeping other parameters fixed, the method achieves clean edits without artifacts or unintended attribute changes, resolving the reliability issue
3Productivity
If minimal labelled data is used for training, then data acquisition cost decreases, but control precision over semantic attributes deteriorates
Solution Approach 1:
The patent applies self-service by enabling the system to generate its own training data through the pre-trained unconditional GAN. The model synthesizes images with embedded semantic attributes and automatically pairs them with generated labels, creating self-sufficient training datasets without external labeling resources. This maintains control precision while maximizing productivity
Solution Approach 2:
The patent uses feedback mechanisms where the auxiliary classifier provides gradient feedback to refine the latent code manipulation. This feedback loop ensures that even with minimal training data, the system learns accurate mappings between latent space transformations and semantic attribute changes, maintaining control precision while achieving high data generation efficiency
Data Source
AI summary
Methods, apparatus and techniques herein relates to determining directions in GAN latent space and obtaining disentangled controls over GAN output semantics, for example, to enable use of such to generating synthesized images such as for use to train another model or create an augmented reality The methods, apparatus and techniques herein, in accordance with embodiments, utilize the gradient directions of auxiliary networks to control semantics in GAN latent codes. It is shown that minimal amounts of labelled data with sizes as small as 60 samples can be used, which data can be obtained quickly with human supervision. It is also shown herein, in accordance with embodiments, to select important latent code channels with masks during manipulation, resulting in more disentangled controls.