Pattern-Aware GANs With Attenuated Embeddings for Sparse Data
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Solution Overview
Problem
Existing generative adversarial networks (GANs) struggle with sparse datasets, particularly in resource-constrained edge devices, due to insufficient training data, leading to unbalanced classes, undetermined parameters, and poor generalization, and traditional data augmentation techniques provide limited additional data.
Innovation Solution
A lightweight GAN system that includes a pattern extractor to extract feature embeddings, which are attenuated through random feature selection or additive white Gaussian noise, and used to condition the generator for generating synthetic data, thereby automating control beyond class labels and modality data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data augmentation techniques are used, then additional data is provided, but the data variety remains limited and overfitting is not effectively reduced
Solution Approach 1:
The generator creates synthetic data copies that replicate the statistical properties and patterns of real data while introducing variations through conditional inputs. These synthetic copies expand the training dataset quantity while maintaining data variety through controlled generation parameters.
Solution Approach 2:
The conditional GAN changes parameters by accepting conditional inputs (class labels, attribute vectors, or other data) that control the generation process. This allows the same generator to produce diverse synthetic data with different characteristics by varying the conditional parameters, thereby increasing data variety.
2Reliability
If more training data is provided to improve generalization, then model performance increases, but computational resources and training time increase
Solution Approach 1:
The system generates synthetic data copies that can be used to expand the training dataset without requiring additional real data collection. This increases the effective training data quantity for improving generalization while avoiding the computational cost of collecting and processing more real-world data.
Solution Approach 2:
The generator is pre-trained to create synthetic data that mimics real data distributions. This preliminary preparation allows the system to generate training data on-demand during model training, reducing the need for large initial datasets and decreasing overall computational resource requirements.
3Ease of operation
If class labels are used as conditioning data, then generator control is improved, but the control mechanism remains manual and limited
Solution Approach 1:
The generator is designed to accept multiple types of conditional inputs including class labels, attribute vectors, and other data modalities. This multi-functional control mechanism allows flexible and sophisticated generator control without increasing system complexity, as the same generator architecture handles different conditioning data types.
Solution Approach 2:
The conditional input acts as an intermediary that translates high-level control requirements into generator instructions. By accepting conditional inputs as intermediaries, the system achieves sophisticated generator control without direct complex manipulation of the generator itself, simplifying the control mechanism.
Data Source
AI summary
A computer-implemented method includes training at least a generative adversarial network, the method operable on one or more processors. The method includes at least (1) applying pattern extraction to a set of training data to extract one or more feature embeddings representing one or more features of the training data, (2) attenuating the one or more feature embeddings to create one or more attenuated feature embeddings, (3) providing the one or more attenuated embeddings to a generator of the generative adversarial network as a condition to at least partly control the generator in generating synthetic data, the providing being performed automatically and dynamically during training of the generator, and (4) with the generator, generating synthetic data based at least in part on the attenuated embeddings.