Pretrained Model Adaptation With Unit-Vector Weight Transforms
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Solution Overview
Problem
Existing methods for adapting pretrained models to specific tasks, such as outputting classifications or generating data, are inefficient in terms of computational resources and risk catastrophic overwriting of weights, leading to suboptimal performance.
Innovation Solution
A method and device that utilize transformations involving unit-length vectors and hyperplane reflections or interactions to adapt pretrained models, using outer products and normalized vectors to learn weight adjustments efficiently, reducing the risk of catastrophic overwriting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods for adapting pretrained models are used, then the model can be adapted to specific tasks, but computational resources are consumed inefficiently and catastrophic overwriting of weights occurs
Solution Approach 1:
The patent transforms the weight adaptation problem by changing the parameter representation from direct weight updates to learning transformation vectors that operate on weights through outer products. This parameter transformation enables efficient adaptation while maintaining weight stability, as the transformation vectors can be learned with fewer computational resources and applied in a controlled manner to avoid catastrophic overwriting.
Solution Approach 2:
The patent introduces transformation vectors as intermediary elements between the original weights and the adapted weights. These vectors serve as mediators that transform the pretrained weights into task-specific weights through outer product operations, enabling gradual and controlled adaptation rather than direct weight overwriting, thus improving both computational efficiency and reliability.
2Adaptability or versatility
If direct weight updating is performed during adaptation, then the model adapts to the task, but the pretrained weights are catastrophically overwritten
Solution Approach 1:
The patent changes the adaptation parameter from direct weight values to transformation vectors. By learning these vectors and applying them through outer products, the model achieves task adaptation while the transformation process inherently stabilizes weight changes, preventing catastrophic overwriting of pretrained knowledge.
Solution Approach 2:
Instead of directly and completely updating all weights (excessive action), the patent uses transformation vectors that apply partial, controlled modifications to the weights. This partial action approach allows the model to adapt to tasks while preserving the stable components of pretrained weights, avoiding catastrophic overwriting.
3Manufacturing precision
If computational resources are allocated for model adaptation, then the model can be fine-tuned, but the resource consumption is excessive
Solution Approach 1:
The patent extracts only the essential transformation vectors from the full weight matrix, rather than processing and updating all weights. By learning and storing only these compact transformation vectors and applying them through outer products, the method achieves precise model fine-tuning with significantly reduced computational resource consumption compared to traditional fine-tuning approaches.
Data Source
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AI summary
Device and method for adapting a in particular pretrained model (106) to a task, wherein the method comprises providing the in particular pretrained model (106), wherein the in particular pretrained model (106) comprises a layer that is configured to map a multidimensional input of the layer depending on weights to a multidimensional output of the layer, wherein a vector comprises a subset of the weights that weighs the elements of the multidimensional input for a dimension of the output of the layer, wherein the method comprises providing training data and learning at least one vector of a transformation for adapting the subset of the weights depending on the training data and the output of the model (106), wherein the at least one vector has unit length, and the transformation comprises an outer product of the at least one vector with the transposed of the at least one vector, or wherein the at least one vector is normalized to have unit length, and wherein the transformation comprises an outer product of the normalized at least one vector with the transposed of the normalized at least one vector.