Neural Decoder Fine-Tuning Without Weight Selection Overhead
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep neural network decoders are suboptimal for single images due to increased code size from transmitting weight updates during fine-tuning, and previous approaches either result in suboptimal weight selection or increased bit length.
Innovation Solution
A method for fine-tuning a subset of parameters in a neural network decoder without transmitting the identifier of these weights, using an optimizer to select and optimize an input-specific subset of weights, applicable to image and sound processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weight updates are transmitted during fine-tuning to improve decoding performance, then decoding accuracy is improved, but bitstream size increases
Solution Approach 1:
The patent extracts and transmits only the essential fine-tuning information (quantized weight updates) rather than the entire weight matrix, significantly reducing the bitstream size while maintaining decoding accuracy improvements
Solution Approach 2:
The patent applies fine-tuning selectively to specific weight matrices or layers that have the most impact on decoding performance, rather than updating all parameters, thus achieving good accuracy with minimal bitstream overhead
2Measurement precision
If all parameters are fine-tuned for each input to optimize performance, then decoding quality is improved, but computational complexity and time increase
Solution Approach 1:
The patent performs partial fine-tuning by updating only a subset of weight parameters rather than all parameters, achieving sufficient decoding quality improvement without the full computational cost of complete fine-tuning
Solution Approach 2:
The patent changes the state of weight parameters through fine-tuning updates, adapting the decoder to specific input characteristics while maintaining a balance between quality improvement and computational efficiency
3Device complexity
If a generic decoder model is used for all inputs, then device complexity is reduced, but performance becomes suboptimal for single images
Solution Approach 1:
The patent introduces dynamic adaptability to the decoder by enabling fine-tuning of weight parameters based on input characteristics, allowing the generic model to adapt to specific inputs without requiring a completely different model architecture
Data Source
AI summary
A deep neural network-based coding system for images determines update parameters of a deep neural network model for decoding an image. These parameters are determined by an encoder and provided to a decoder to update the model of the decoder before decoding the image. This provides structural sparsity by fine-tuning only some parameters of the neural decoder. The update is done on a set of parameters selected based on the embedding representative of the coded image so that there is no need to transmit information related to the selection of the parameters to be updated. A more generic optimizer/inference engine is also described as well as an application to sound upsampling.