Deep Learning Model Generation of Samples with Enhanced Attributes
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Solution Overview
Problem
Deep generative neural networks face challenges in generating data samples with enhanced attributes beyond the training distribution, limiting their ability to create new data with improved properties.
Innovation Solution
The Enhanced Sample Generation (ESG) module uses a framework that includes a generator and a discriminator, employing techniques such as contrastive objectives, smoothing objectives, and cycle-consistency learning to enhance attribute values in the latent space, allowing for the generation of samples with improved attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep generative neural networks are trained on a given training distribution, then they can generate realistic data samples within that distribution, but they fail to generate new data samples with enhanced attributes beyond the training distribution
Solution Approach 1:
The patent introduces a discriminator as an intermediary component that guides the generator to produce samples with enhanced attributes. The discriminator evaluates generated samples and provides feedback through the loss function, enabling the generator to iteratively improve and exceed the training distribution while maintaining sample quality.
Solution Approach 2:
The patent implements a feedback mechanism where the discriminator's predictions are used to update the generator via backpropagation. This continuous feedback loop allows the generator to learn from evaluation results and progressively generate samples with enhanced attributes beyond the original training distribution.
2Quantity of substance
If conventional upsampling or downsampling methods are used to handle imbalanced classes, then class distribution is adjusted, but the underlying data quality and attribute enhancement remain limited
Solution Approach 1:
The patent changes the fundamental parameter being optimized from class balance to attribute enhancement. Instead of merely adjusting class frequencies through resampling, the generator is trained to maximize desirable attributes (e.g., sentiment score, protein stability) directly, transforming the optimization target from distributional balance to quality improvement.
Solution Approach 2:
The patent replaces mechanical resampling operations (upsampling/downsampling) with a learning-based generation system. Rather than physically duplicating or removing samples, the neural network learns to synthesize new samples with enhanced attributes, substituting mechanical data manipulation with intelligent data creation.
3Adaptability or versatility
If data-driven design methods iterate sampling and updating, then the generator learns to assign higher probability to desirable inputs, but the process is computationally intensive and converges slowly
Solution Approach 1:
The patent performs preliminary action by pre-training the generator on the base distribution before implementing the attribute enhancement phase. This staged approach allows the generator to first learn basic data generation capabilities, then focus optimization efforts on enhancing specific attributes, reducing overall computational iterations needed.
Solution Approach 2:
The patent introduces dynamics by making the training process adaptive and stage-based. The system transitions from static training on fixed distributions to dynamic training where the target distribution evolves based on attribute enhancement goals, allowing efficient convergence at different training stages.
4Reliability
If smoothing is applied to label and features of training data to improve regression generalization, then interpolation and extrapolation are enhanced, but the method does not specifically target attribute enhancement in generative models
Solution Approach 1:
The patent applies local quality by focusing enhancement on specific attribute dimensions rather than uniformly smoothing all features. The discriminator is trained to evaluate and guide improvement of particular attributes (e.g., sentiment, stability) while preserving other important characteristics of the data, enabling targeted attribute enhancement.
Solution Approach 2:
The patent segments the data representation into latent factors that correspond to different attributes. By operating in this segmented latent space, the generator can independently manipulate and enhance specific attributes without affecting other properties, enabling precise attribute-specific enhancement.
Data Source
AI summary
Embodiments described herein provide methods and systems for generating data samples with enhanced attribute values. Some embodiments of the disclosure disclose a deep neural network framework with an encoder, a decoder, and a latent space therebetween, that is configured to extrapolate beyond the attributes of samples in a training distribution to generate data samples with enhanced attribute values by learning the latent space using a combination of contrastive objective, smoothing objective, cycle consistency objective, and a reconstruction loss.