Frequency-Domain Model Adapters to Prevent Mode Collapse
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large machine learning models face challenges in fine-tuning due to overfitting and generate outputs that are highly similar (mode collapse) and biased towards training data characteristics, lacking diversity.
Innovation Solution
Implementing low-rank adapter operations in the frequency domain, using Fourier transforms and inverse transforms, along with frequency masking to reduce bias and improve output diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fine-tuning is applied to large machine learning models, then model adaptability improves, but overfitting occurs and output diversity decreases
Solution Approach 1:
The model parameters are segmented into two distinct sets: frozen base model parameters and trainable adapter parameters. This segmentation allows the model to adapt to new tasks through the adapter while the base model remains unchanged, preventing overfitting and maintaining output diversity. The adapter is a small module with significantly fewer parameters than the full model, enabling efficient fine-tuning without catastrophic forgetting.
Solution Approach 2:
Instead of uniformly updating all model parameters, the invention applies local quality by selectively training only the adapter parameters while keeping the base model parameters frozen. This localized parameter update approach allows task-specific adaptation without affecting the general knowledge stored in the base model, thereby maintaining output diversity and preventing overfitting.
2Adaptability or versatility
If personalized adapters are trained for fine-tuning, then model adaptability improves, but mode collapse occurs and generated outputs become highly similar
Solution Approach 1:
The invention introduces dynamic frequency masking that adapts during the generation process. The masking strategy dynamically adjusts which frequency components are suppressed based on the current generation state, preventing the model from collapsing into a single mode while maintaining task-specific adaptation through the trainable adapter parameters.
Solution Approach 2:
The invention transforms the problem from spatial domain to frequency domain using Fourier transforms, and applies parameter changes by selectively masking different frequency components. This frequency-domain approach with adaptive masking prevents mode collapse by ensuring diverse frequency representations in the generated outputs, while the trainable adapter maintains task adaptability.
3Productivity
If adapters are heavily biased towards fine-tuning data characteristics, then training efficiency improves, but output diversity substantially reduces
Solution Approach 1:
The frequency masking mechanism acts as an intermediary between the trainable adapter and the generation process. It mediates the influence of training data characteristics by selectively suppressing frequency components that lead to bias, while preserving the efficient task-specific adaptation provided by the adapter. This intermediary layer maintains training efficiency while preventing excessive bias towards training data.
Solution Approach 2:
The invention creates a composite structure combining the trainable adapter (for task-specific efficiency) with frequency masking (for diversity preservation). This composite approach integrates two different mechanisms: the adapter provides efficient fine-tuning while the frequency masking component prevents over-specialization, achieving both training efficiency and output diversity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The approach reduces mode collapse and generation bias, enhancing output diversity and accuracy by transforming and masking specific frequencies associated with training data attributes.
Implementation Method 1
generating a first frequency tensor based on processing the first feature tensor using a Fourier transform operation
Implementation Method 2
generating a third feature tensor based on processing the first transformed frequency tensor using an inverse Fourier transform operation
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, a first feature tensor is accessed as input to a portion of a machine learning model. A second feature tensor is generated based on processing the first feature tensor using the portion of the machine learning model, and a frequency tensor is generated based on processing the first feature tensor using a Fourier transform operation. A transformed frequency tensor is generated based on processing the frequency tensor using a trained adapter corresponding to the portion of the machine learning model. A third feature tensor is generated based on processing the transformed frequency tensor using an inverse Fourier transform operation. A fourth feature tensor is generated as output from the portion of the machine learning model based on aggregating the second and third feature tensors.