Neural Network Parameter Optimization via Meta-Learning for Media Compression
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Solution Overview
Problem
Existing technologies for optimizing neural network parameters in media item compression, such as image and video encoding, face challenges in efficiently updating parameters to achieve balanced compression quality and bitrate.
Innovation Solution
The proposed solution involves a meta-learning approach, specifically using Model-Agnostic Meta-Learning (MAML), to initially train encoder and decoder neural networks to optimize further learning during the inference phase. This includes overfitting selected parameters, such as encoder and decoder weights, or latent tensors, to improve compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional neural network parameter optimization methods are used in media item compression, then the compression quality can be improved, but the computational complexity and training time increase significantly
Solution Approach 1:
The patent applies preliminary action by performing Meta-Learning during the training phase to prepare the neural network for rapid adaptation during inference. The MAML algorithm pre-trains the encoder and decoder networks with meta-updates, enabling them to quickly adapt to specific content during compression without requiring extensive retraining at inference time. This resolves the contradiction by shifting computational complexity to the training phase while achieving fast, high-quality compression during inference.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting neural network parameters through meta-learning. The system maintains a set of initial parameters trained with MAML and allows rapid parameter adaptation during inference based on the specific input content. This enables the network to optimize compression quality for different media items while keeping the overall system complexity manageable through efficient parameter updates.
2Productivity
If neural network parameters are optimized for specific content during encoding, then compression efficiency improves, but the encoding time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network with Meta-Learning during the offline training phase. This preparation enables the network to perform fast content-specific adaptation during encoding without requiring extensive computation at encoding time. The MAML-pretrained network can quickly adjust to specific content characteristics, achieving high compression efficiency while minimizing encoding time.
3Adaptability or versatility
If more parameters are updated during inference, then the adaptation to specific content improves, but the bitrate overhead increases
Solution Approach 1:
The patent applies local quality by selectively updating only the most relevant neural network parameters during inference based on the specific input content. Rather than updating all parameters uniformly, the system identifies and adjusts only those parameters that provide the most benefit for the current content type, reducing the amount of data that needs to be transmitted while maintaining effective content adaptation.
Data Source
AI summary
In example embodiments, an apparatus, a method, and a computer program product are provided. An example apparatus include processing circuitry; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processing circuitry, cause the apparatus at least to: overfit a neural network on each media item, from a batch of media items, for a number of iterations to obtain an overfitted neural network model for the each media item; evaluate the overfitted neural network model on the each media item to obtain evaluation errors; and update parameters of the neural network to be based on the evaluation errors.