Transfer Learning via Power Means Embedding Transformation
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Solution Overview
Problem
Existing machine learning models face challenges in efficiently fine-tuning pre-trained neural networks on edge devices due to resource constraints, particularly in handling high-dimensional time-series data, where the aggregation of temporal information is often lost, and the dimensionality of embeddings is fixed, making it difficult to adapt to varying resource budgets.
Innovation Solution
The proposed method involves using power means functions to transform embeddings from pre-trained neural networks, allowing for resource-aware dimensionality reduction while preserving temporal information, by selecting a subset of power means functions based on available resources and concatenating them to produce more expressive embeddings that can be fine-tuned on edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If embeddings from pre-trained neural networks are used for transfer learning on edge devices, then model performance can be improved, but resource constraints on edge devices make fine-tuning difficult
Solution Approach 1:
The patent extracts only the necessary embedding representations from the pre-trained neural network's intermediate layers, rather than transferring or fine-tuning the entire complex model. This extraction approach isolates the valuable temporal features while leaving the resource-intensive model architecture on the server, resolving the contradiction between model performance and device complexity.
Solution Approach 2:
The patent transforms the embedding data by applying power means functions to aggregate temporal information across the time dimension, creating a new dimensional representation that preserves temporal patterns while reducing complexity. This dimensional transformation enables effective transfer learning on resource-constrained devices without requiring full model fine-tuning.
2Measurement precision
If high-dimensional time-series data is processed to capture temporal information, then model accuracy improves, but computational resources required increase
Solution Approach 1:
The patent introduces power means functions as intermediary operations between the raw high-dimensional time-series embeddings and the final aggregated representation. These intermediary functions efficiently aggregate temporal information across the time dimension while consuming fewer computational resources than traditional deep learning temporal processing methods.
Solution Approach 2:
The patent changes the aggregation parameter by using power means functions with variable exponents to aggregate embeddings over the time dimension. This parameter-based aggregation captures temporal patterns effectively while requiring significantly fewer computational resources compared to maintaining full high-dimensional time-series data throughout the network.
3Loss of information
If embeddings are transformed using multiple power means functions, then temporal feature richness increases, but processing time and complexity increase
Solution Approach 1:
The patent applies a selective subset of power means functions rather than exhaustively applying all possible aggregation operations. This partial action approach captures the essential temporal features needed for the task while avoiding the excessive processing time that would result from applying all possible power means functions or deeper temporal aggregations.
Data Source
AI summary
There is provided an apparatus, method and computer program for a network node comprising access to a pre-trained neural network node model, for causing the network node to: receive, from an apparatus, a request for a first plurality of embeddings associated with an intermediate layer of the neural network node model; and signal said first plurality of embeddings to the apparatus.