Multi-Level Autoencoder Compression With Correlation-Based Data Recovery
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Solution Overview
Problem
Existing multi-layer autoencoder architectures for data compression and restoration primarily focus on compression and do not fully exploit correlations within data, limiting their effectiveness in achieving high compression ratios while preserving important information.
Innovation Solution
A multi-layer autoencoder system with a correlation network that combines convolutional layers, pooling layers, and activation functions, where the autoencoder compresses data and a correlation network leverages patterns and similarities to restore decompressed data, jointly optimized using loss functions for reconstruction and restoration quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multi-layer autoencoder is used for data compression, then compression ratio is improved, but information loss increases
Solution Approach 1:
A correlation network is introduced as an intermediary component between the encoder and decoder. This correlation network processes multiple compressed representations and identifies patterns and relationships among them, using this correlated information to guide the reconstruction process and recover lost details in the decompressed output.
Solution Approach 2:
The patent combines multiple compressed representations of the same input data, which are generated by processing different batches or views of the data through the autoencoder. These multiple compressed representations are fed into the correlation network together, allowing the system to leverage redundant and complementary information across representations to improve reconstruction quality.
2Manufacturing precision
If correlation network is added to exploit data patterns, then restoration quality is improved, but device complexity increases
Solution Approach 1:
The correlation network is designed with a universal architecture that can handle multiple compressed representations of varying dimensions and types. It processes batches of compressed data and generates correlation-aware features that can be applied to improve reconstruction across different data types and compression levels, making the added complexity beneficial across multiple scenarios.
3Measurement precision
If multiple compressed representations are processed, then restoration accuracy is improved, but processing time increases
Solution Approach 1:
The system processes multiple compressed representations (excessive action) to improve restoration accuracy, but implements batch processing and efficient correlation computation to manage the additional processing time. The correlation network processes batches of compressed representations in parallel where possible, and the multiple representations are used to extract more robust correlation patterns that improve restoration accuracy.
Data Source
AI summary
Compressing and restoring data using a multi-level autoencoder and a correlation network. The multi-level autoencoder compresses (in the encoder section) and decompresses (in the decoder section) data streams, and a separate correlation network trained on groups of input data sets restores lost data by leveraging correlations between the data sets.