Latent-Space Data Generation with Semantic Noise Prediction
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Solution Overview
Problem
Conventional simulators generate fake images that are difficult to apply in real-world scenarios like autonomous driving and element defect detection, and designing diverse three-dimensional objects is resource-intensive.
Innovation Solution
A data generation framework that includes obtaining a latent representation from training data, combining it with noise to generate a noisy latent representation, and updating a prediction model using semantic mask data to minimize prediction errors, thereby generating more realistic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional simulators generate fake images in virtual scenarios, then the generation process is simple and resource-efficient, but the realism and applicability to real-world scenarios deteriorates
Solution Approach 1:
The patent introduces a prediction model as an intermediary component between the encoder and decoder. This prediction model predicts noise from noisy latent representations and guides the denoising process, enabling the system to generate realistic data while maintaining a manageable framework structure. The prediction model acts as a mediator that learns the noise distribution and facilitates realistic data generation without requiring complex three-dimensional object designs.
2Reliability
If diverse three-dimensional objects are designed to reduce the difference between simulated and real data, then the realism of generated data improves, but the cost and resources required deteriorates
Solution Approach 1:
The patent uses latent representations as compressed copies of real data characteristics instead of requiring full three-dimensional object designs. The encoder learns to extract essential features from real data and represents them in a compressed latent space. This copying approach preserves the essential characteristics needed for realistic data generation while avoiding the resource-intensive process of designing diverse three-dimensional objects.
Solution Approach 2:
The patent changes the parameters of the data generation process by working in latent space rather than manipulating full three-dimensional objects. By operating with compressed latent representations and learning noise distributions in this transformed parameter space, the system achieves realistic data generation with reduced computational resources compared to designing and rendering diverse three-dimensional objects.
3Reliability
If noise is added to latent representation to enable noise removal learning, then the ability to generate realistic data improves, but the complexity of the training process deteriorates
Solution Approach 1:
The patent performs preliminary noise addition to latent representations during the training phase. By pre-addding controlled noise to the latent space before the denoising process, the system prepares the data in advance for the prediction model to learn the noise distribution. This preliminary action simplifies the overall training process compared to more complex iterative denoising approaches, as the noise characteristics are established upfront and can be learned directly by the prediction model.
Data Source
AI summary
A method and an apparatus related to a data generation framework are provided. A latent representation is obtained from training data by an initial encoder. The latent representation and first noise data are combined to generate a noisy latent representation. The noisy latent representation is input to a prediction model, and an initial prediction corresponding to the noisy latent representation is output by referring to first semantic mask data by the prediction model, wherein the first semantic mask data defines one or more first semantic categories for the training data. The prediction model is updated according to a prediction error between the initial prediction and the first noise data to generate a trained prediction model. Therefore, data that is close to the real world may be generated.