Neural Network Data Coding Pipeline Fine-Tuning
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Solution Overview
Problem
Current data coding pipelines for machine learning tasks face challenges in efficiently fine-tuning and adapting to various tasks, leading to increased iteration requirements and reduced efficiency in data compression and reconstruction for both human and machine consumption.
Innovation Solution
A data coding pipeline comprising a feature extractor neural network, an encoder neural network, and a decoder neural network is developed, which determines multiple losses corresponding to different tasks and updates weights to reduce the number of iterations for fine-tuning, enabling faster adaptation and improved performance for specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data coding pipelines are used for machine learning tasks, then data compression and reconstruction can be performed, but the number of iterations required for fine-tuning increases and efficiency decreases
Solution Approach 1:
The patent applies preliminary action by pre-training the feature extractor neural network on a large dataset before fine-tuning for specific tasks. This pre-training establishes a strong initial feature representation that reduces the number of iterations needed during subsequent task-specific fine-tuning, thereby improving fine-tuning efficiency and reducing iteration time
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the learning rate and other training parameters during the fine-tuning process. By optimizing these parameters based on the pre-trained state, the system accelerates convergence and reduces the number of iterations required to achieve task-specific performance
2Adaptability or versatility
If multiple tasks are supported in the data coding pipeline, then versatility improves, but the complexity of determining and updating weights across multiple tasks increases
Solution Approach 1:
The patent implements universality by designing a single feature extractor neural network that serves multiple tasks simultaneously. The network learns task-agnostic features during pre-training that can be adapted to various downstream tasks, enabling multi-functionality without requiring separate networks for each task
Solution Approach 2:
The patent applies segmentation by separating the feature extraction functionality from task-specific processing. The feature extractor network is divided into reusable feature extraction layers that can be shared across multiple tasks, reducing the overall complexity of managing multiple task-specific weight sets
3Reliability
If the coding pipeline is fine-tuned for specific tasks, then performance on those tasks improves, but the number of iterations required increases
Solution Approach 1:
The patent uses preliminary action through pre-training on a large, diverse dataset before task-specific fine-tuning. This creates a robust foundation that requires fewer iterations to adapt to specific tasks, maintaining high task performance while reducing fine-tuning time
Solution Approach 2:
The patent applies local quality by selectively fine-tuning only the portions of the network most relevant to each specific task while keeping other parts frozen. This localized approach achieves task-specific performance optimization without requiring extensive iterations across the entire network
Data Source
AI summary
Example embodiments provide a system for training a data coding pipeline including a feature extractor neural network, an encoder neural network, and a decoder neural network configured to reconstruct input data based on encoded features. A plurality of losses corresponding to different tasks may be determined for the coding pipeline. Tasks may be performed based on an output of the coding pipeline. A weight update may be determined for at least a subset of the coding pipeline based on the plurality of losses. The weight update may be configured to reduce a number of iterations for fine-tuning the coding pipeline for one of the tasks. This enables faster adaptation of the coding pipeline for one of the tasks after deployment of the coding pipeline. Apparatuses, methods, and computer programs are disclosed. Apparatuses, methods, and computer programs are disclosed.