MODEM Framework Adapting Generative Models to Target Marginals
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Solution Overview
Problem
Deep generative models struggle to adapt to distribution shifts in real-world scenarios, such as seasonal changes or counterfactual simulations, as they are typically trained on pre-collected data that may not align with new target distributions, leading to inefficiencies in retraining and loss of correlations between set elements.
Innovation Solution
The Module-Oriented DivErgence Minimization (MODEM) framework adapts pre-trained generative models by updating specific modules to align with target marginal constraints, preserving previously learned correlations while efficiently generating new distributions, applicable to latent variable, autoregressive, and energy-based models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep generative models are retrained on new data to adapt to distribution shifts, then the model can align with target distributions, but the computational cost and time increase significantly
Solution Approach 1:
The generative model is divided into multiple independent modules that can be selectively updated. Instead of retraining the entire model, only the relevant modules are adjusted to adapt to distribution shifts, significantly reducing computational cost and time while maintaining adaptability to target distributions.
Solution Approach 2:
The model is pre-trained on source distribution data to learn general patterns and correlations. This preliminary training establishes a foundation that can be later adapted to target distributions through selective module updates, avoiding the need for complete retraining when distribution shifts occur.
2Adaptability or versatility
If deep generative models are retrained on new data to adapt to distribution shifts, then the model can align with target distributions, but the computational resources and energy consumption increase
Solution Approach 1:
The model architecture is segmented into modular components with distinct functions. When adapting to target distributions, only the necessary modules are retrained, dramatically reducing energy consumption compared to full-model retraining while achieving the required alignment with target distributions.
Solution Approach 2:
Different modules of the generative model are assigned different update strategies based on their specific functions and importance. Critical modules that directly impact distribution alignment are updated, while less critical modules are preserved, optimizing energy usage while maintaining adaptability.
3Adaptability or versatility
If the entire generative model is retrained to adapt to target distributions, then the model alignment improves, but the previously learned correlations between set elements may be lost
Solution Approach 1:
The model is segmented into modules responsible for different aspects of data generation. Modules that capture correlations between set elements are identified and protected from updates, while only modules directly responsible for distribution alignment are retrained, preserving valuable correlation information while achieving target distribution alignment.
Solution Approach 2:
Different protection levels are applied to different model modules. Modules that encode important correlation structures are marked as protected and excluded from retraining, while other modules are updated to adapt to target distributions, thereby preserving information while enabling adaptation.
Data Source
AI summary
The present disclosure is directed to generative models for datasets constrained by marginal constraints. One method includes receiving a request to generate a target dataset based on a marginal constraint for a source dataset. A first object occurs at a source frequency in the source dataset. The marginal constraint indicates a target frequency for the first object. The source dataset encodes a set of co-occurrence frequencies for a plurality of object pairs. A source generative model is accessed. The source generative model includes a first module and a second module that are trained on the source dataset. The second module is updated based on the marginal constraint. An adapted generative model is generated that includes the first module and the updated second module. The target dataset is generated based on the adapted generative model. The first object occurs at the target frequency in the target dataset. The target dataset encodes the set of co-occurrence frequencies for the plurality of object pairs.