Importance Masking for Neural Network Parameter Finetuning
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Solution Overview
Problem
Current multimedia transport technologies face challenges in effectively defining and utilizing masks and ordering lists for neural network parameter importance, leading to inefficiencies in model finetuning and weight update processes.
Innovation Solution
An apparatus and method that learn the importance of parameters using a training dataset, define masks or ordering lists to indicate parameter importance, and share these with encoders and decoders for model finetuning, enabling efficient weight update and compression processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all parameters are updated during model finetuning, then model performance is improved, but computational resources and time are wasted on updating less important parameters
Solution Approach 1:
The patent segments parameters into different importance groups using importance masks and ordering lists. Parameters are divided into fine-tunable parameters (important) and frozen parameters (less important), allowing selective updating of only critical parameters during finetuning, thus reducing time while maintaining performance.
Solution Approach 2:
The patent applies local quality by treating different parameters differently based on their importance. Important parameters receive full updates with gradient descent, while less important parameters are either frozen or updated with reduced magnitude, optimizing the finetuning process by allocating computational resources where they matter most.
2Measurement precision
If all parameters are transmitted in bitstream, then decoder accuracy is improved, but transmission bandwidth is wasted on less important parameters
Solution Approach 1:
The patent extracts and transmits only the most important parameters and their weight updates in the bitstream. By using importance masks and ordering lists to identify critical parameters, the system transmits a subset of parameters that maintains decoder accuracy while significantly reducing transmission data volume.
Solution Approach 2:
The patent applies partial action by transmitting only the necessary portion of parameters required for accurate decoding. Instead of sending all parameters, the system sends a carefully selected subset based on importance metrics, achieving sufficient accuracy with reduced bandwidth consumption.
3Productivity
If importance masks and ordering lists are implemented, then finetuning efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing importance masks and ordering lists before the finetuning process. These importance indicators are calculated in advance based on parameter significance, allowing the finetuning algorithm to efficiently select which parameters to update without complex real-time decisions, thus improving efficiency while managing system complexity.
4Productivity
If weight updates are compressed, then transmission efficiency is improved, but reconstruction accuracy at decoder may deteriorate
Solution Approach 1:
The patent applies local quality by applying different compression strategies to different parameters based on their importance. Critical parameters undergo minimal or no compression to preserve accuracy, while less critical parameters are compressed more aggressively, achieving overall transmission efficiency without significantly compromising reconstruction accuracy of important parameters.
Data Source
AI summary
An example apparatus includes at least one processor; and at least one non-transitory memory comprising computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform; learn importance of one or more parameters by using a training dataset; define one or more masks for indicating the importance of the one or more parameters for a model finetuning; share at least one mask of the one or more masks with at least one of an encoder or a decoder; finetune at least one parameter of the one or more parameters based at least on the at least one mask; send or signal one or more weight updates corresponding to the at least one parameter in a bitstream to the decoder.